Jobs at Djinni

We're looking for an AI & Data Engineer to build the data and AI backbone of a livestream shopping and social marketplace platform for the Middle East - one that combines real-time auctions and social engagement into a single automated, data-driven product.

The audience pipelines, personalization logic and AI agents for this platform don't exist yet. You build them from scratch and own how they run in production.

What you'll do

Growth Data Activation (MarTech)

• Build automated audience pipelines from the warehouse into marketing platforms - Meta CAPI, Google Ads, Braze, Customer.io, WhatsApp API.

Lifecycle Automation & Personalization

• Design behavior-based triggers (cart abandonment, livestream reminders) and ranking/recommendation logic for products and live shows.

Practical AI Integration

• Deploy production-ready LLM workflows and AI agents for operational needs - seller onboarding, content moderation, automated customer support - with proper cost and evaluation guardrails.

Who we're looking for

Must have:

• 3-4 years of experience in Data Engineering, Software Engineering, or Data Architecture.
• Strong, production-grade Python and SQL.
• Proven experience building OLAP data warehouses (ClickHouse, BigQuery, Snowflake, Redshift etc)
• Experience with event tracking and identity resolution - Segment, RudderStack, PostHog.
• Practical experience integrating LLMs/RAG into real production systems - not just prompts or demos.

We are looking for a Finance Manager or Financial Analyst who can take ownership of the company's financial reporting, cost tracking and financial analytics.

The ideal candidate should have experience with DTC / E-commerce and payment processing, or be willing to quickly learn the specifics of this area.

The key requirement is the ability to understand the real economics of the business, including not only revenue and advertising costs, but also payment processing fees, refunds, chargebacks, alerts and other payment-related expenses.

Key Responsibilities

1. Financial Reporting & Analytics

• Build and maintain regular P&L reports.
• Build and maintain Cash Flow reports.
• Track revenue, COGS, gross profit, operating expenses and net profit.
• Track actual vs. planned expenses.
• Prepare weekly and monthly financial reports.
• Analyze profitability by product, GEO, channel or project when required.
• Build consolidated financial dashboards and reporting tables.

2. Payment & Processing Analytics

The candidate should understand or be willing to learn:

• Credit card transactions.
• Payment processing fees.
• MDR / processing fees.
• Refunds.
• Chargebacks.
• Chargeback alerts.
• Failed / declined transactions.
• 3DS-related costs.
• PSP / payment gateway fees.
• MID-related costs.
• Rolling reserves.
• Payment provider payouts and settlements.

It is important to understand the full cost of each payment-related event, rather than simply recording the transaction amount.

For example, for a refund, the model should account for the refunded amount as well as any non-refundable processing fees, fulfillment/shipping costs and other associated expenses where applicable.

For chargebacks, the model should account for the lost transaction amount, chargeback fees and other related costs.

3. Data Collection & Consolidation

The Finance Manager will collect and consolidate financial data from multiple sources, including:

• Bank accounts.
• PSPs and payment providers.
• Payment gateways.
• CRM / order management systems.
• Advertising platforms.
• Meta / Google and other traffic sources.
• Accounting systems.
• Internal spreadsheets and reports.

The goal is to have one reliable financial model where all major business expenses and revenues are reflected.

4. Reconciliation

• Reconcile bank transactions with internal financial data.
• Reconcile PSP settlements and payouts.
• Track refunds and chargebacks.
• Verify processing fees.
• Identify discrepancies between systems.
• Track pending balances and reserves.
• Monitor actual cash received vs. expected cash.
• Investigate and explain financial discrepancies.

5. Operating Expenses & Payroll

The financial model should include all relevant business expenses, including:

• Employee salaries.
• Contractor payments.
• Bonuses and commissions.
• Advertising spend.
• Payment processing.
• Refunds.
• Chargebacks.
• Chargeback alerts.
• Fulfillment and shipping.
• Product / manufacturing costs.
• SaaS and software.
• Accounting and legal expenses.
• Other operational expenses.

Financial Models & KPIs

The candidate should be able to build and maintain reporting covering:

• Revenue.
• Net Revenue.
• Refunds.
• Chargebacks.
• Chargeback alerts.
• Payment processing fees.
• COGS.
• Advertising spend.
• Fulfillment / shipping.
• Payroll.
• Operating expenses.
• Gross Profit.
• Operating Profit.
• Net Profit.
• Cash In.
• Cash Out.
• Cash Balance.
• Cash Flow.

Key ratios and metrics should also be tracked, including:

• Refund rate.
• Chargeback rate.
• Alert rate.
• Processing cost %.
• Advertising cost %.
• Payroll %.
• Operating expense %.
• Net margin.
• ROAS / ROI.
• Contribution margin.

Requirements

Must have:

• Strong Excel / Google Sheets skills.
• Strong financial analysis skills.
• Ability to build and maintain P&L and Cash Flow models.
• Ability to work with large amounts of data.
• Strong attention to detail.
• Ability to reconcile data from multiple sources.
• Good understanding of business and unit economics.
• Ability to independently identify discrepancies and explain financial results.

Strong plus:

• Experience in DTC / E-commerce.
• Experience with subscription businesses.
• Experience with payment processing.
• Experience working with PSPs / MIDs / payment gateways.
• Understanding of refunds, chargebacks and chargeback alerts.
• Experience with Meta / Google advertising spend.
• Experience building financial models from scratch.

Important

We are not necessarily looking for someone who already knows every detail of payment processing.

A strong financial analyst with excellent Excel / Google Sheets skills, good financial thinking and experience building P&L / Cash Flow models can be a good fit if they are willing to learn the payment side of the business.

The person should be able to quickly understand the flow:

Customer payment → PSP / MID → processing fees → refund / alert / chargeback → reserves → settlement → actual cash received

The key requirement is that the person does not simply enter numbers into spreadsheets, but understands what each number means and how it affects the actual profitability and cash flow of the business.

Expected Result

The Finance Manager should provide a clear and accurate picture of the business:

How much we generated → how much we actually received → where the money was spent → payment-related costs → refunds / chargebacks → operating expenses → payroll → actual profit → actual cash flow.

The person should be able to independently maintain this system, identify discrepancies and provide clear weekly and monthly financial reports to management.

ITKingdom is an online coding school for kids ages 8–18. We’re looking for a Roblox Studio Teacher to join our team.

You’ll teach one-on-one online lessons in English and help students learn coding while building their own Roblox projects.

No sales. No finding students. We provide the students, lesson materials, and support.

💎 What You’ll Get

• Ready-to-go students — no need to find your own clients.
• Flexible schedule — work part-time or full-time.
• Ready-made lesson materials — no need to build every lesson from scratch.
• Support from a methodologist and team lead whenever you need it.
• Professional development — workshops, knowledge sharing, and opportunities to grow.
• Reliable payments — twice a month.

• Long-term work with a supportive team.

   

🎯 What We’re Looking For

You:

• Have English at B2 level or higher (must-have)
• Have some programming knowledge and hands-on experience.
• Are willing to learn Roblox Studio — we’ll teach you how to use it.
• Enjoy working with kids and can explain things in a simple, easy-to-understand way.
• Are reliable and responsible.
• Speak Ukrainian.

• Are open to feedback and willing to keep learning.

   

🖥️ What You’ll Need

• Computer or laptop
• Camera

• Stable internet connection

   

🕒 Schedule

Our students are in Ukraine, the US, and Canada, so lesson times vary.

Kyiv time:

• Weekdays: 5:00 PM–2:00 AM

• Saturday & Sunday: 11:00 AM–9:00 PM

   

You can choose the hours that work best for you within these time slots.

🚀 Sound Like You?

If you enjoy coding, like working with kids, and want to help them turn their ideas into Roblox projects, we’d love to hear from you!

We are looking for an experienced and product-minded Lead Data Scientist to drive the evolution of our Data Science function, shape the technical vision, and guide a high-impact team working at the intersection of research and production. This is a unique opportunity to influence strategy, lead cross-functional teams, and build ML solutions that directly power mission-critical business decisions

What You Will Do

• Define and own the Data Science strategy, ensuring alignment with business goals and long-term product vision.
• Shape and maintain the technical roadmap based on business priorities, team capacity, and company growth.
• Scale the Data Science function as the organization expands, designing team structure and hiring profiles.
• Lead, hire, and mentor a cross-functional team of Data Scientists while fostering a strong R&D culture built on rigor and measurable impact.
• Manage resources across both Delivery (S&D) and Research (R&D) squads, ensuring clarity and productivity.
• Act as the bridge between R&D and Delivery: translate business needs into research tasks and adapt prototypes into production-ready solutions.
• Architect client solutions by selecting appropriate models, algorithms, and assortment strategies using our ML stack.
• Serve as the quality gatekeeper: review code, validate A/B test designs, and ensure all work meets the team’s Definition of Done (DoD).
• Drive key DS metrics such as research-to-production time, experiment velocity, and recommendation quality.
• Communicate insights clearly to product managers, engineers, clients, and stakeholders, aligning expectations and next steps.
• Represent the company’s technological excellence to investors and partners during high-stakes due diligence.
• Collaborate with clients' Data Science and Analytics teams (including PhDs), explaining methodology, assumptions, and results with confidence.
• Translate complex model outputs into concise, business-oriented presentations for executives and decision-makers.
• Support the sales and pre-sales process by building compelling technical narratives and showcasing ML capabilities.

• Stay hands-on when needed — from debugging critical delivery issues to prototyping new models or designing experiments.

   

What You Have

• 5+ years of experience in Data Science or a related field, with a strong record of delivering production value.
• Strong Python and SQL proficiency, with clean and modular coding practices.
• Hands-on experience with Databricks and Apache Spark.
• Familiarity with Data Mesh principles and collaboration workflows with data engineering teams.
• Solid mathematical foundation, ideally within a Computer Science–related discipline.
• Expertise in scientific Python tools: NumPy, pandas, scikit-learn, and either TensorFlow/Keras or PyTorch.
• Deep understanding of statistical methods and A/B testing frameworks.
• Experience with Time Series Forecasting approaches.
• 3+ years working with tabular and mixed (multimodal) data.
• Bonus: experience in Causal Inference and ecommerce/retail domains.

• Upper-intermediate or higher English proficiency and excellent public speaking skills.

   

Soft Skills

• A strong focus on business impact — understanding not only how the model works but why it matters.
• Ability to translate complex concepts into simple explanations for non-technical stakeholders.
• Professional communication with highly technical client teams, including PhD-level experts.
• Ownership of data requirements, integration workflows, and validation processes.
• Comfort with experimentation, iteration, and decision-making in a dynamic environment.
• Proactivity: contribute DS-driven ideas to the Product Backlog and influence roadmap direction.
• Creative thinking and a pragmatic approach to solving complex technical and product challenges.

• Curiosity, eagerness to learn, and a strong entrepreneurial mindset.

   

You Will Love Working With Us Because

• Innovative ML stack with freedom to choose the best tools and approaches.
• Remote-first culture with the flexibility to work from anywhere.
• Flexible working hours (start between 8–11 AM), no time tracking.
• Regular performance reviews and clear OKR structure.
• In-depth onboarding with transparent success milestones.
• We cover 70% of your training or course fees.
• 20 vacation days, 15 days off, and an additional week of paid Christmas holidays.
• 20 business days of paid sick leave.
• Partial medical insurance coverage.

AI-First Senior Software Engineer

Ukraine, Kyiv  On-Site · Full-Time

Company: Orbox.ai - AI-Native PropTech Operating System for Real Estate

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Tech Lead

Works with: AI-Engineer, Product Analyst, UI/UX Designer, QA Engineer

PropTech experience: Not essential, very good if has direct or indirect experience

English: Advanced (B2-C1)

About

We are building an AI-native operating system for real estate. The product is a PropTech platform where LLMs and AI agents are not bolted on top - they are the architecture. Owners, operators and guests interact with the AI system through natural language. AI autonomously generates compliance documents, routes staff, answers guest queries from a RAG-indexed knowledge base in real time and orchestrates financial split-payments.

But the product is only half of it.

We are equally an AI-first delivery organization. The way we discover, define and ship is as differentiated as what we ship. Specs, stories and designs are machine-readable artifacts our coding and review agents consume directly - and senior engineers are the ones who design the system, direct the agents and own every line that reaches production.

The role

This is the role that turns machine-readable specs into production software - by leading AI agents, not by typing every line yourself.

You will report directly to the Head of Delivery & Product, with short decision loops to founders and direct access to product, design and QA.

This is a hands-on senior engineering role at the core of the delivery machine. Most of our code is written by AI coding agents against machine-readable specs. That makes senior engineering judgment more valuable, not less: agents write fast, but still pick wrong abstractions, miss edge cases, leak data across tenants and ship code that passes its own tests and breaks in production. You are the engineer who designs the system, sets the guardrails and owns what gets merged.

You will build and ship features end to end across backend, frontend and integrations: service design, data models, APIs, UI, government and third-party integrations, AI features (LLM agents, RAG, document generation) and the infrastructure they run on.

You will work side by side with the AI-First Product Analyst (who writes the specs), Product Designers (who own the mockups) and QA (who holds the release bar). You will use our shared Claude Team / Claude Code with premium seating along with the company's AI tools daily, deeply and natively - not as a novelty, but as the default way you work.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today (boilerplate, refactors, tests, migrations, docs), which it does not (architecture, security boundaries, trade-off calls, debugging subtle production issues) - and how to combine both deliberately.

What you will own & do

AI-First Engineering

• Daily, deep, native use of AI coding agents for implementation from specs, refactoring, test writing, code review, debugging and documentation.

• Treat agents like a team you lead: clear specs in, reviewed code out - you own the context, rules files, guardrails and the review bar.

• Build and improve our agentic delivery pipeline: repo conventions, agent instructions, MCP tooling and CI checks that keep agent output safe to merge.

• Contribute to internal evals from the engineering side: define what “good” looks like for AI-generated code and for our user-facing AI features.

Product Engineering

• Own features end to end: from spec & acceptance criteria to design, implementation, tests, deployment and monitoring on prod.

• Design clean service boundaries, data models and APIs for a multi-tenant B2B2C platform with role-based access.

• Build complex frontend surfaces: dashboards, real-time data, multi-step workflows, back-office tooling.

• Integrate with government and regulatory APIs, payment providers and third-party systems - reliably, idempotently, with proper error handling and audit trails.

AI & LLM Systems

• Build production AI features: LLM agents, tool calling, RAG pipelines, compliance document generation - with grounding, fallbacks and cost control.

• Engineer the non-deterministic layer: prompt versioning, structured outputs, guardrails against hallucinations and prompt injection, tenant-safe retrieval.

• Instrument AI behaviour with tracing, logging and metrics - treat AI output as something to be measured, not trusted.

Engineering Quality & Ownership

• Own the merge bar: review agent and human PRs, enforce architecture, security and performance standards.

• Write automated tests and CI that make agent output safe to ship; partner with QA on test strategy and release readiness.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec layer, not hacked around in code.

• Shape the engineering practice at an early stage: conventions, environments, observability, incident handling - lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

• 4+ years of hands-on software engineering on production web products, with senior-level ownership of a system or major product area.

• Strong full-stack skills: modern backend (TypeScript / Node.js or Python), modern frontend (React / Next.js or similar) and relational databases (PostgreSQL or similar).

• Daily, deep, native use of AI coding tools (Claude Code, Cursor or similar) in your engineering workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Hands-on experience building LLM-based features: agents, tool calling, RAG, structured outputs - shipped to production, not just demos.

• Solid system design: multi-tenant SaaS, role-based access, API design, async processing, third-party integrations.

• Critical code review skills: you read AI-generated code with suspicion and catch what agents miss - security holes, data leaks, wrong abstractions, hidden complexity.

• Cloud & DevOps fluency: Docker, CI/CD, at least one major cloud (AWS / GCP / Azure), logging and monitoring.

• 0 → 1 mindset: comfortable in an early-stage team, working directly with founders and product, shipping fast without losing rigor.

• Strong written and verbal communication in English - including specs, PR descriptions and technical discussions.

Strong advantage (Nice to have)

• Background in PropTech, hospitality tech, Fintech or multi-sided marketplace platforms.

• Exposure to payment flows, compliance / regulatory workflows, document-generation systems or government API integrations.

 Experience with LLM evals and observability (Langfuse, LangSmith or similar) and vector databases.

• Real-time systems, IoT or tokenization / blockchain infrastructure.

Why this role

You will help build the first AI-native operating system for real estate in Dubai and define how software gets built when AI agents write most of the code.

• Genuine engineering ownership.

• Clear growth trajectory - Tech Lead / Engineering Lead.

• Direct access to founders and Head of Delivery & Product.

• Technically ambitious product surface - AI-native, Agentic system, RAG, IoT, real-time systems, government APIs, tokenization.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior Software Engineer

Ukraine, Kyiv · On-Site · Full-Time

Company: Orbox Group - new AI-native product for the oil & petroleum business in Ukraine

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Tech Lead

Works with: Business Analyst, UI/UX Designer, QA Engineer, business owners & operations team

Languages: English - Advanced (B2-C1); Ukrainian - fluent

The role

This is the role that turns a real offline business into production software - by leading AI agents, not by typing every line yourself.

This is a hands-on senior engineering role. Most of our code is written by AI coding agents against machine-readable specs. That makes senior engineering judgment more valuable, not less: agents write fast, but still pick wrong abstractions, miss edge cases, get money and inventory maths wrong and ship code that passes its own tests and breaks in production. You are the engineer who designs the system, sets the guardrails and owns what gets merged.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today (boilerplate, refactors, tests, migrations, docs), which it does not (architecture, security boundaries, trade-off calls, debugging subtle production issues) - and how to combine both deliberately.

What you will own & do

AI-First Engineering

• Daily, deep, native use of AI coding agents for implementation from specs, refactoring, test writing, code review, debugging and documentation.

• Treat agents like a team you lead: clear specs in, reviewed code out - you own the context, rules files, guardrails and the review bar.

 Set up the agentic delivery pipeline for the new product from day one: repo conventions, agent instructions, MCP tooling and CI checks that keep agent output safe to merge.

• Contribute to internal evals from the engineering side: define what good” looks like for AI-generated code and for the product's user-facing AI features.

Product Engineering

• Own the architecture from zero: service boundaries, domain and data model, APIs, environments - built for a real operational business with multiple sites and roles.

• Own features end to end: from spec & acceptance criteria to design, implementation, tests, deployment and monitoring on prod.

• Build operational surfaces: dashboards, inventory and stock movements, orders, pricing, documents, approvals, reporting and back-office tooling - including mobile-friendly flows for people in the field.

Integrations & Data

• Integrate with the existing world: accounting and ERP systems, Excel and legacy data, banks and payment providers, fiscalization and regulatory reporting.

• Connect the physical side of the business where relevant.

• Plan and run data migration from spreadsheets and legacy tools - reliably, idempotently, with proper error handling and audit trails.

AI & LLM Systems

• Build production AI features: LLM agents, tool calling, RAG, document processing (waybills, invoices, contracts), reconciliation, forecasting and anomaly detection - with grounding, fallbacks and cost control.

• Engineer the non-deterministic layer: prompt versioning, structured outputs, guardrails against hallucinations and prompt injection, role-safe access to data.

• Instrument AI behaviour with tracing, logging and metrics - treat AI output as something to be measured, not trusted.

Engineering Quality & Ownership

 Own the merge bar: review agent and human PRs, enforce architecture, security and performance standards.

 Write automated tests and CI that make agent output safe to ship; partner with QA on test strategy and release readiness.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec layer with the Business Analyst, not hacked around in code.

• Shape the engineering practice for the product: conventions, environments, observability, incident handling - lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

 4+ years of hands-on software engineering on production web products, with senior-level ownership of a system or major product area.

 Strong full-stack skills: modern backend (TypeScript / Node.js or Python), modern frontend (React / Next.js or similar) and relational databases (PostgreSQL or similar).

• Daily, deep, native use of AI coding tools (Claude Code, Cursor etc.) in your engineering workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Hands-on experience building LLM-based features: agents, tool calling, RAG, structured outputs - shipped to production, not just demos.

• Solid system design for operational B2B software: transactional data, inventory and money flows, role-based access, API design, async processing, third-party integrations.

• Critical code review skills: you read AI-generated code with suspicion and catch what agents miss - security holes, data leaks, wrong abstractions, hidden complexity.

• Cloud & DevOps fluency: Docker, CI/CD, at least one major cloud (AWS / GCP / Azure), logging and monitoring.

• 0 → 1 mindset: comfortable starting a product from an empty repo, working directly with founders, business owners and product, shipping fast without losing rigor.

• Strong written and verbal communication in English and Ukrainian - including specs, PR descriptions, technical discussions and talking to the people who run the business.

Strong advantage (Nice to have)

• Experience integrating with BAS / 1C, Ukrainian banks and payment providers, fiscalization or tax / excise reporting.

• Document processing (OCR, extraction), forecasting or anomaly detection in production.

• Experience with LLM evals and observability (Langfuse, LangSmith or similar) and vector databases.

Why this role

You will own a new AI-native/AI-first engineering from an empty repo and prove that an AI-first delivery machine can transform a traditional industry, not just a startup niche.

• Genuine engineering ownership - you design the system from zero and hold the merge bar.

 A real business as your first customer - live operations, real users, real volumes and money.

• Clear growth trajectory - Tech Lead / Engineering Lead for this product line.

• Direct access to founders and Head of Delivery & Product.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior Business Analyst

Ukraine, Kyiv · On-Site  Full-Time

Company: Orbox

Product: AI-native software digitalizing an existing offline business

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Head of Delivery & Product

Works with: Business owners & operations team, Engineering team, Design team, AI agents

Languages: Ukrainian - native / fluent; English - Upper-Intermediate+ (B2+)

The role

This is the role that defines what this new product is, why each part of it is worth building and how it is described to the machines that build it.

You will report directly to the Head of Delivery & Product and sit in one team with Orbox - same AI-first delivery machine, same tools, same standards. You will own product from the analysis side: discovery, process analysis, PRDs and agent-ready specifications.

This is a hands-on, 0 → 1 analytical role. You will dig into where the work happens. You will map as-is workflows, find where time, money leak and design the to-be processes where AI agents and software do the routine work and people make the decisions.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which analysis and documentation tasks AI does well today, which it does not and where in a traditional business AI creates real value versus where it is just noise.

What you will own & do

AI-First Way of Working

• Daily, deep, native use of AI tools for interview and field-note synthesis, process mapping, document analysis, PRD and spec drafting, competitor teardowns and data analysis.

• Treat AI tools the way an engineer treats their IDE: opinionated, tuned, integrated into every step of your workflow.

• Co-design the boundary between what you produce by hand and what is generated, transformed or validated by agents.

• Contribute to internal evals from the analysis side: define what “good” looks like for AI-generated artifacts and for the product's user-facing AI features.

Business Discovery & Process Analysis

• Own end-to-end discovery of the existing business: stakeholder and staff interviews, shadowing, field visits, document and data audits across departments.

• Map as-is workflows (BPMN or similar) and design to-be processes - roles, handoffs, documents, approvals, exceptions and the data behind each step.

• Find and size the opportunities: manual work, errors, delays, fuel and cash losses, reporting pain - and where AI agents can take over routine work (document processing, reconciliation, dispatch, forecasting, anomaly detection).

• Understand the regulatory and accounting side of the business (excise, fiscalization, tax and fuel reporting, primary documents) well enough to turn it into requirements.

• Evidence over opinion: maintain a living research repository so every product decision traces back to a real workflow, a real number or a real user.

PRDs & Specs for the AI-First Delivery Machine

• Own the PRD & spec layer for the product: vision, scope, modules, user stories, acceptance criteria, edge cases, data contracts and state behaviour.

• Convert validated requirements into clear, structured, machine-readable specifications our orchestrator and coding agents can implement, test and evolve reliably.

• Define the domain model: products, tanks and depots, vehicles and routes, counterparties, contracts, prices, documents - the single source of truth for engineering, QA and evals.

• Keep specs in sync with reality: when the business or product changes, the spec changes first.

Delivery, Rollout & Adoption

• Shape the roadmap and scope with the Head of Delivery & Product and business owners - what we digitalize first and why.

• Plan integrations and migration from existing tools and legacy systems (Excel, accounting software, equipment and sensor data where relevant).

 Run UAT with real users in the business, define success metrics per capability and track what actually changed after release.

• Drive adoption: training materials, rollout plans and feedback loops with staff who have worked offline for years.

What we are looking for

Must have

• 4+ years of relevant experience as a business analyst, systems analyst or product analyst, with senior-level ownership of discovery and requirements.

• Digital transformation experience: you know how to take real offline or legacy business processes (operations, logistics, finance, supply chain) and turn them into working software together with an engineering team.

• 0 → 1 builder: comfortable being the first and only analyst on a new product (for the start), working directly with business owners, Heads and engineering.

• Strong process analysis craft: interviews, shadowing, BPMN / process modeling, as-is / to-be design, requirements that hold up in the real world.

• Excellent product documentation: PRDs, user stories, acceptance criteria, edge cases, data models - clear, testable, unambiguous, structured enough that engineers (and agents) do not have to guess.

• Daily, deep, native use of AI tools in your analysis workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Comfortable with complex operational B2B domains: inventory, logistics, back-office, finance and accounting flows, role-based access, reporting.

• Data literacy: comfortable reading data (Excel, SQL or BI tools) to back decisions and quantify the problem.

• Strong communication in Ukrainian and English (B2+): you can interview a depot operator in the morning and write a spec for an international engineering team in the afternoon.

Strong advantage (Nice to have)

• Knowledge of Ukrainian regulatory and accounting specifics: excise, fiscalization, tax reporting, primary documents; experience with BAS / 1C or ERP implementations.

• Experience defining specs or workflows for AI agents, RAG systems or document processing (OCR, extraction).

Why this role

• Genuine product ownership - you define the product from discovery to specs.

• A real business as your first customer - live operations, real users, real numbers.

• Clear growth trajectory: evolves into AI-First Product Manager and then Head of Product for this product line.

• Direct access to founders and Head of Delivery & Product.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior QA Engineer (Manual)

Ukraine, Kyiv · On-Site · Full-Time

Company: Orbox.ai - AI-Native PropTech Operating System for Real Estate

Location: Ukraine, Kyiv  on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Technical Lead 

Works with: Product Analyst, Product Designer, Engineering team, Fleet of Agents

PropTech experience: Not essential; very good if you have direct or indirect experience

English: Advanced (B2+)

About

We are building an AI-native operating system for real estate. The product is a PropTech platform where LLMs and AI agents are not bolted on top - they are the architecture. Owners, operators and guests interact with the AI system through natural language. AI autonomously generates compliance documents, routes staff, answers guest queries from a RAG-indexed knowledge base in real time and orchestrates financial split-payments.

But the product is only half of it.

We are equally an AI-first delivery organization. The way we discover, define and ship is as differentiated as what we ship. Specs, stories and designs are machine-readable artifacts our coding and review agents consume directly — and quality is verified by a human who knows exactly where agents fail, where specs lie and where the product breaks in real hands.

The role

This is the role that decides whether what we ship actually works  for humans and for the agents that built it.

You will report directly to the Head of Delivery & Product, with short decision loops to founders and direct access to engineering.

This is a hands-on, manual-first QA role at the core of the delivery machine. Most of our code is written by AI agents against machine-readable specs. That makes human quality judgment more valuable, not less: agents pass their own checks and still ship broken flows, wrong edge-case behaviour and UX that technically matches the spec but fails the user. You are the last honest reader of the product before it reaches staging review and then customers on prod.

You will own manual testing end to end: exploratory testing, spec-based test design, UAT coordination, regression passes and release sign-off. You will test not just deterministic UI flows, but AI-driven behaviour  agent responses, RAG answers, document generation  where “expected result” is a judgment call, not an assert.

You will work side by side with the AI-First Product Analyst (who writes the specs you test against) and engineering (whose agents implement them). You will use our shared Claude Team / Cowork with premium seating along with the company's AI tools daily, deeply and natively — not as a novelty, but as the default way you work.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which QA tasks AI does well today (test-case generation, coverage analysis, log triage), which it does not (exploratory judgment, UX intuition, risk feel) — and how to combine both deliberately.

What you will own & do

AI-First Way of Working

• Daily, deep, native use of AI tools for test-case generation from specs, coverage gap analysis, bug report drafting, log and session triage, regression checklist upkeep.

• Treat AI tools the way an engineer treats their IDE: opinionated, tuned, integrated into every step of your workflow.

• Contribute to internal evals from the QA side: define what “good” looks like for AI-generated code, AI-generated test artifacts and our user-facing AI features.

Manual Testing & Test Design

• Own manual testing end to end: exploratory sessions, spec & story-based test design, edge cases, negative paths, cross-role and multi-tenant scenarios.

• Convert machine-readable specs & stories with acceptance criteria into structured, traceable test cases and checklists — every requirement maps to a verification.

• Run deep exploratory testing where scripted coverage ends: real-world flows, messy data, weird devices, impatient users.

• Verify complex B2B2C surfaces: dashboards, role-based access, real-time data, payment flows, document generation, back-office tooling.

Testing AI-Driven Behaviour

• Test the non-deterministic layer: agent conversations, autonomously generated compliance documents — correctness, groundedness, tone, failure modes.

• Design and run structured evaluation of LLM outputs with the Analyst and engineering: treat AI output as something to be measured, not trusted.

• Hunt for hallucinations, prompt-injection surfaces, permission leaks across tenants and roles, and silent agent failures.

Quality Process & Release Ownership

• Own release readiness: regression scope, smoke passes, UAT coordination with stakeholders, GO/NO-GO input for every release.

• Write bug reports engineers (and agents) can act on without a follow-up question: environment, steps, expected vs actual, evidence, severity.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec/story layer, not patched in QA.

• Build the QA practice from scratch: process, tooling, test data, defect workflow in Jira — lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

 4+ years of hands-on manual QA experience on web products, with senior-level ownership of quality for a product or major product area.

• 0 → 1 builder: comfortable being the first and only QA, setting up the practice yourself, working directly with founders and engineering — not joining an already-scaled QA org.

• Experience testing LLM-based features, chatbots, RAG systems or agent workflows — including designing evals or output rubrics.

• Daily, deep, native use of AI tools in your QA workflow: test generation, triage, analysis, documentation. You have a real, opinionated workflow built around them and can defend your choices.

• Excellent test design craft: from acceptance criteria to test cases, edge cases, negative scenarios and risk-based prioritization — structured, traceable, unambiguous.

• Strong exploratory testing instincts: you find the bugs no script would — and can explain how you found them.

• Sharp written bug reporting and strong verbal communication in English (C1+), under startup timelines, without losing rigor.

• Comfortable with complex B2B / multi-sided product surfaces: multi-tenant SaaS, role-based access, real-time data, payments, back-office tooling.

 Working knowledge of APIs and databases for verification: reading API responses (Postman or similar), basic SQL to check data behind the UI, reading logs.

Strong advantage (Nice to have)

• Background in PropTech, hospitality tech, Fintech or multi-sided marketplace platforms.

• Exposure to payment flows, compliance / regulatory workflows or document-generation systems.

• Test automation literacy (Playwright, Cypress or similar) — enough to direct agents that write automation, even if you don't write it daily.

Why this role

You will help build the first AI-native operating system for real estate in Dubai and define what quality means when AI agents write most of the code. Both are differentiators, both are hard and both are yours to own at the senior level.

• Genuine quality ownership — you build the QA practice from zero and hold the release bar. This is not a “execute someone else's test cases” role.

• Clear growth trajectory — this role evolves into AI-First QA Lead / Head of Quality as the team scales.

• Direct access to founders and Head of Delivery & Product — short decision loops, no middle management.

• Technically ambitious product surface  AI-native, Agentic system, RAG, IoT, real-time systems, government and regulatory APIs and eventually tokenization infrastructure.

 AI-first by default — you will co-design how quality assurance works inside an AI-first delivery machine.

• Great office in the centre of Kyiv — one on-site team, short feedback loops, fast decisions.

The automotive industry moves trillions a year and still runs on a phone queue, a web form and a salesperson who calls back tomorrow. A car is sold, financed, serviced and traded the way it was in 1914, with a website on top. We have set out to put that industry on autonomous rails: commercial operations that run themselves. AI employees that talk to every customer in seconds, in their language, on every channel, that decide, act, book straight into the systems the industry runs on, and get better every day. Dealer groups first. Then the importer, the OEM, the fleet, the finance company, every place a vehicle meets a customer.

It already runs: about a thousand real customer conversations a day for BMW, MINI, Ford and Hyundai dealer groups, and a pilot with a Mercedes-Benz group in Germany. What powers it is not a chatbot. It is a distributed system with language models as components inside it: state, queues, retries, dealer systems that answer in fifteen seconds or not at all, a hundred ways for an output to pass every check and still be wrong. For the next stage we are looking for a strong team member at co-founder level who wants to go deep on autonomous systems, model development, system design, and the real, live deployment of a new breed of system, and who takes the hardest problems because they are the hardest. Flat hierarchy: you are treated as an equal member of the team and your reward is linked to the outcome. The team pushes the frontier relentlessly. There are no meetings and no politics. There is a system in production, customers on the other end, and the next thing to ship.

What you will build and do

1. Own the agent runtime end to end: orchestration, tool execution, memory, handover to humans, integrations into dealer management systems and messaging channels, in production for real customers from your first month.
2. Build evals and traces before features. Every change to an agent ships with a way to prove it did not make things worse.
3. Decide the architecture with the founders: what runs where, which model does which job, what a session may cost, what happens when a component fails.
4. Ship every week. The product is in production; the customers are dealer groups whose customers are waiting on the other end.

Who we look for

1. Has built AI systems from the ground up, not wrappers over an API. Strong senior experience shipping systems in production. A PhD is a plus, not a requirement.
2. Sees an agentic system as a distributed system with the LLM as one component or orchestrator inside it. Thinks in state, failure modes, retries and observability, not just prompts.
3. Uses coding agents effectively as a daily working tool.
4. Builds evals and traces before building features. Understands that a large share of failures here are semantic, not mechanical: output can pass every check and still be wrong.
5. Conscious of cost and latency: how many tokens, how many dollars per session, where a smaller model does the job.
6. Comfortable with incomplete specs. Makes decisions with missing information, documents them, and keeps moving. This is an early stage startup; nobody will tell you what to do step by step.
7. Wants a flat hierarchy and means it: takes on the hardest problems, argues for what is right, and carries the outcome as an equal member of the team, with a reward linked to that outcome.
8. Wants to go deep: autonomous systems, model development and fine tuning, system design, and deployments that real customers depend on the same day. No meetings, no politics, shipping.

What we offer

Role

Full time, remote from wherever you build best, with time together in person when it matters.

Pay

A fixed salary and a share in the company that links you to the outcome, discussed openly in the first conversation.

Work

Direct work with the founder and our AI lead, who built the current system. No meetings, no politics. A product in production with the most complete automotive AI reference in the region; your code answers real customers the week it ships.

How we work

01 Flat hierarchy

You are an equal member of the team. The best argument wins, whoever makes it.

02 Rewarded on the outcome

A share in the company links you to what we build, not to a title.

03 No meetings, no politics

Written decisions, a system in production, customers on the other end.

04 Ship every week

Your work answers real customers the week it lands. We push the frontier and measure it.

How to apply

One page: the best AI system you built from the ground up and what it did in production, how you evaluated it, and the first thing you would change about how agentic systems are usually built. A link to code we can read beats any cover letter. 

Cars have been sold and serviced the same way since 1914: a showroom, a phone queue, a form, a salesperson who calls back tomorrow. Salesteq builds the autonomous commercial system that replaces that: AI employees that answer every customer in seconds on the website, WhatsApp, Instagram, Messenger and the phone, in Arabic, English and German, qualify the demand, win back what was lost, and book the workshop straight into the dealer's own systems. The dealer keeps the customer. The machine does the work.

This is not a plan. It runs today across BMW, MINI, Ford and Hyundai in Saudi Arabia, with about a thousand real customer conversations a day, with a BMW dealer in Switzerland, and in pilot with a Mercedes-Benz group in Germany. The next step is the layer every surface reads from: a customer asks ChatGPT, Google or a voice assistant, and the dealer's stock, prices and diary answer. Whoever builds that layer for Europe's dealer groups decides how cars are sold on this continent for the next twenty years. Nobody has yet built the machine that makes the European market come to us. That is this job.

What you will build and do

1. Own European revenue end to end: awareness, pipeline, meetings, pilots, signed contracts. First markets Germany, Switzerland, Austria, then the Netherlands and the UK.
2. Build the machine: the dealer group universe, outbound a group owner answers, case stories, LinkedIn, a newsletter to dealer groups, AI native demo sites per prospect, with AI agents doing the volume work and you deciding what runs.
3. Walk into the room: meetings with owners and managing directors in German and English, the demo, the compliance conversation, the pilot, the group wide contract.
4. Measure everything: cost per conversation, per meeting, per pilot. Kill what does not work, scale what does.

Who we look for

1. Someone who loves to invent and build. Started things, shows numbers and failures in public, left somewhere because it was too slow.
2. Has built a growth engine at a B2B company and closed with it, or sold their own product as a founder. Can show the dashboard and three signed deals with numbers.
3. Already runs their own work with AI tools and agents, and wants to run what a team would run. The founder coaches the stack personally.
4. German at negotiation level is a plus, English fluent. The largest market and the reference are German speaking.
5. Sells what runs. Automotive is learnable; how a family owned dealer group buys must be understood by month three.

What we offer

Role

Full time, any location in the CET time zone from which the job can be done, travel to dealer groups when it matters.

Pay

A fixed salary, an uncapped commission on every contract the customer pays, and share options in the company building this.

Work

Direct work with the founder, who joins every first meeting until you have closed one, and with a product team that ships to production every week.

How we work

01 Flat hierarchy

You are an equal member of the team. The best argument wins, whoever makes it.

02 Rewarded on the outcome

A share in the company links you to what we build, not to a title.

03 No meetings, no politics

Written decisions, a system in production, customers on the other end.

04 Ship every week

Your work answers real customers the week it lands. We push the frontier and measure it.

How to apply

One page: the best thing you ever built and what it produced, three deals you closed with numbers, and the first thing you would build here. No cover letter. 

Responsibilities:

• Work in SCRUM team 
• Design and develop high-load microservices
• Produce high-quality code covered with different levels of tests
• Maintain services on production and assure their stability and performance
• Provide code review for teammates 
• Contribute to the Company’s development processes and practices 

Requirements:

• 5+ years of professional Java development experience, including at least 2 years in a product company or product-focused environment
• Proficiency in Java 17+ and modern backend development practices
• Solid experience with the Spring Framework and Spring Boot
• Practical experience designing and developing microservices
• Experience with MySQL, Redis, and other SQL or NoSQL databases
• Experience with Kafka and event-driven architectures
• Experience building REST APIs and working with Protobuf
• Hands-on experience with SQL and database schema design
• Experience with CI/CD practices and tools such as GitLab CI
• Ability to write unit and integration tests using tools such as Mockito, WireMock, and Testcontainers
• Strong understanding of SOLID principles, clean code, and common software design patterns
• Experience with Git and modern software development workflows
• Excellent troubleshooting and problem-solving skills
• Good communication and collaboration skills
• English proficiency at Intermediate level or higher

AI-enabled software development

• Hands-on experience using AI coding agents in day-to-day software development.
• Ability to break down engineering goals into clear, well-scoped tasks for AI agents.
• Ability to provide effective context, constraints, acceptance criteria, and validation steps.
• Strong awareness of the risks and limitations of AI-generated software.
• Ability to distinguish where AI can accelerate delivery and where human judgment is essential.
• Interest in exploring and improving agentic development workflows.
• Willingness to share effective workflows and lessons learned across engineering team.

Would be great to have:

• Experience with Aerospike
• Experience with Spring Cloud
• Experience with Docker and Kubernetes
• Experience developing and operating high-load distributed systems
• Experience with Google Cloud Platform (GCP) and its core services, including BigQuery

What we offer:

• International company with several offices in different locations : Tel-Aviv, Kyiv, Warsaw, Limassol.
• Granting an Option;
• A professional environment with great people to work with;
• Competitive salary, performance review;
• Flexible working hours, no one here tracks your work time because our culture is built on trust and cooperation;
• Social and welfare packages;
• 20 working days paid vacation, official public holidays and 100% sick leave coverage;
• Opportunities to make a difference, to develop and grow;
• Regular corporate celebrations, team building and fun activities;
• Comfortable office in the city center. Our Kyiv office is open 24/7 with all safety and health measures in place.

We are looking for an SMM & Content Producer to join our team and take ownership of content creation and social media activities. 

In this role, you will turn the company’s culture, day-to-day life and projects into engaging digital content, manage the full content production cycle, and use data to continuously improve content performance. 
 

You will work closely with employees, HR, Marketing, and other teams to create relevant content and maintain a consistent Tone of Voice and visual identity across our digital channels.
 

Responsibilities
 

• Develop and execute content plans across key careers social media channels (Instagram, LinkedIn, Telegram, etc.)
• Create video, photo, and written content from concept to publication
• Identify employee stories and create UGC content that showcases our people, culture, and projects
• Manage social media content calendars and coordinate content distribution across channels
• Ensure content follows the company’s Tone of Voice and visual guidelines
• Monitor social media trends and identify relevant content formats and opportunities
• Track content performance and use analytics to optimize engagement and candidate conversion
   

Requirements
 

• 3+ years of experience in SMM, content production, or a similar role
• Portfolio showcasing your previous work
• Experience managing LinkedIn, Instagram, Facebook, Telegram
• Understanding of social media analytics and key content performance metrics (Meta Business Suite and LinkedIn Analytics)
• Strong hands-on experience with short-form video production, including scripting, shooting, and editing
• Strong copywriting, storytelling, and visual content creation skills
• Proficiency in design tools such as Figma, Canva, Photoshop, or Illustrator, along with hands-on experience using video editing software (e.g., CapCut).
• Strong communication and collaboration skills, with the ability to engage employees and internal teams in content creation
• Upper-Intermediate level of English

Will be a plus
 

• Experience in employer branding, HR, or recruitment marketing
• Experience working in a regulated industry (e.g., fintech, banking, or trading), where content is subject to compliance review
• Familiarity with paid social advertising platforms (e.g., Meta Ads, LinkedIn Campaign Manager)
• Experience creating and adapting content for international, multi-country audiences

We offer
 

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

We are looking for a strategic, hands-on, and data-driven Performance & Talent Strategy Lead to build and scale our performance evaluation and career development ecosystem from scratch.   
Your main mission: Build a clear, fair, and transparent evaluation and career growth system across the company—ensuring top performers receive differentiated rewards and every employee has a well-defined path for advancement.

Responsibilities

- Design and implement a clear, structured Performance Review process, 360° feedback, and goal-tracking methodology from scratch. 
- Lead vendor evaluation, contracting, configuration, and ongoing administration of the new performance management tool. 
- Monitor workflow adoption to ensure HRBPs and managers execute reviews on time and adhere to established standards. 
- Establish calibration guidelines, resolve evaluation disputes. 
- Build and digitize transparent career tracks, job levels, and competency matrices so every employee understands their growth opportunities. 
- Act as the primary owner of the talent pipeline—managing HiPo dashboards, tracking PDP completion, and preventing high-potential employees from feeling "stuck". 
- Oversee internal mobility, navigate cross-team talent transfers smoothly, and build succession planning models. 
- Digitize review outputs into actionable executive dashboards to inform strategic decision-making. 
- Hire, mentor, and lead a dedicated 2-person team (Performance Process Specialist and Career & Promotion Specialist) as the function scales.

Requirements

- 2–3+ years of experience launching and managing performance evaluation cycles (KPI/OKR, 360° feedback, calibration).   
- Practical experience in selecting, configuring, and automating Performance Management Systems.   
- Ability to design job maps, grading systems, competency matrices, and clear promotion criteria for both Tech and Non-Tech roles.   
- Solid understanding of HiPo identification, Career Pathing, 9-Box Grid, and succession planning for key positions.   
- Strong analytical mindset to digitize performance data, create executive dashboards, and drive objective, data-backed decisions.   
- Upper-Intermediate (B2+) level of English for effective communication across global teams.   
- Basic knowledge of evaluation ethics and data privacy standards (GDPR).

We Offer

- 10 paid sick leave days per year;
- 20 paid vacation days per year;
- Public holidays as per the company’s approved Public holiday list
- Medical insurance
- Opportunity to work remotely
- Professional education budget
- Language learning budget
- Wellness budget (gym membership, sports gear and related expenses)

We build AI-heavy software for commercial real estate lenders, covering loan origination, servicing, document extraction, and financial analysis.

We’re looking for an AI Product Delivery Manager to own the path from requirements → development → QA → release.

This is not a traditional PM or project coordinator role. We use AI agents to do the mechanical work — drafting tickets, documentation, test cases, and other delivery tasks. Your job is to define the work precisely, build effective agent workflows, and apply judgment to what comes back.

Schedule — read before applying

This role connects a European development team with a US business team:

• 10:00–12:00 Kyiv time — development team, Mon–Fri
• 19:00–21:00 Kyiv time — US team, Mon–Fri

Both are fixed. The hours between are flexible. Apply only if this schedule works for you long term.

What you’ll own

• Product delivery: sprint planning, backlog, scope, priorities and capacity
• Requirements: feature specs, acceptance criteria, and keeping wiki/designs/tickets aligned
• AI workflows: build agents that generate tickets, documentation, test plans, etc.
• QA: test new features, run regression, validate AI document extraction, and file defects with evidence
• Design review: compare implementation against Figma and catch gaps before release
• Product: participate in scoping and design decisions and represent the user

What we’re looking for

• 3+ years in product management, delivery management, product operations, QA, or a combination
• AI is already part of your daily workflow
• Hands-on experience with AI agents / Claude Code / MCP / Claude Skills / Cowork or comparable tools
• You can write specifications precise enough for an agent to produce useful output
• Strong judgment about when AI output needs verification — and you actually verify it
• Comfortable with Jira, Confluence/wiki, Figma, PDFs, spreadsheets and structured data
• Strong spoken and written English and Russian
• Comfortable running calls with both technical and business teams

Not a fit if your background is primarily marketing, growth, sales, or content.

Nice to have

• Commercial real estate, lending, fintech or insurance
• QA experience
• SQL or basic Python
• Document-heavy B2B software

How we work

AI tools are paid for and expected to be used daily. Small teams, short release cycles, direct access to customers, and a lot of autonomy.

You’ll be the connection point between the European development team and the US business team.

Employment: [Contract / EOR / Full-time]
Benefits: [Benefits, equipment budget, learning budget]

Project duration -  3 months+
Location - EU Only


Responsibilities

• Manage and optimise the Snowflake data warehouse, including query performance, warehouse sizing, resource utilisation, and cost efficiency.
• Maintain the reliability and performance of dbt projects, including data models, automated testing, documentation, and deployment processes.
• Oversee ELT pipelines, ensuring data availability, freshness, and compliance with established SLAs.
• Implement monitoring, alerting, and incident management processes, including root cause analysis and preventive measures.
• Establish data quality standards, automated validation, and observability practices across the data platform.
• Manage access controls, data governance policies, and compliance requirements.
• Monitor Snowflake consumption, implement cost optimisation strategies, and support platform budget planning.
• Develop operational runbooks, CI/CD workflows, and best practices for data engineering teams.
• Collaborate with Data Engineering, Analytics, BI, Security, and Finance teams to improve platform reliability and operational efficiency.

Requirements

• Extensive experience in Data Engineering, Analytics Engineering, DataOps, or Data Platform Operations.
• Strong hands-on experience with Snowflake administration, performance tuning, warehouse management, and SQL optimisation.
• Advanced proficiency in dbt Core or dbt Cloud, including incremental models, macros, testing, and deployment.
• Proven experience managing production ELT pipelines using Fivetran, Airbyte, Stitch, or similar solutions.
• Solid understanding of DataOps principles, CI/CD, version control, automated testing, and deployment pipelines.
• Experience implementing data quality frameworks, monitoring, alerting, and incident response processes.
• Understanding of data governance, access management, and data platform reliability.

Nice to Have

• Experience with data observability tools such as Monte Carlo, Elementary, or Datafold.
• Familiarity with workflow orchestration platforms such as Airflow, Dagster, or Prefect.
• Experience optimising cloud data platform costs, including Snowflake credit consumption and resource allocation.
• Knowledge of regulatory and compliance frameworks, including GDPR and SOC 2.