Jobs Data Engineer

About the role

As a Senior Frontend Engineer on the Payments Platform team, you'll build scalable, high-performance frontend infrastructure and user experiences that power payment flows for global merchants. You'll work closely with product, design, backend, and customer-facing teams to ship reliable, elegant, developer-friendly solutions used across multiple products and integrations. The role combines strong product thinking with engineering excellence — from building reusable UI systems and SDKs to optimizing performance, reliability, and scalability across our platform.

Responsibilities

• Design and develop scalable, performant, maintainable frontend applications using React, TypeScript, and modern web technologies.
• Build reusable UI components, shared libraries, and SDKs that support multiple products and integrations.
• Collaborate closely with Product, Design, Backend, and Customer-facing teams.
• Own features end-to-end — technical planning, architecture, implementation, testing, deployment, and monitoring.
• Optimize application performance, responsiveness, and reliability across devices and browsers.
• Code reviews, testing, documentation, and observability.
• Publish and manage SDK and NPM package versions, including backward compatibility and release management.

• Improve development workflows, tooling, and engineering best practices across the frontend ecosystem.

   

Requirements

• 5+ years of experience building modern web applications.
• Strong proficiency in React, TypeScript, JavaScript, HTML, CSS.
• Experience designing and maintaining reusable frontend architectures, component libraries, or SDKs.
• Experience managing NPM packages, versioning, and release processes.
• Experience building and maintaining frontend integrations with global payment platforms (Adyen, Stripe, Nuvei) — hosted components, wallets, alternative payment methods.
• Strong understanding of frontend performance optimization and modern browser behavior.
• Experience working with APIs and complex asynchronous flows.

• Excellent written and verbal English.

   

Nice to have / Advantage

• Familiarity with Next.js (SSR / App Router).
• Familiarity with additional frontend frameworks (Angular, Vue, etc.)
• Experience with cloud environments and CDN infrastructure (AWS preferred).

• Experience working in production-scale environments with high reliability requirements.

   

Overview

We are looking for a Senior Data Engineer with expertise in scalable data platforms and distributed systems to join our dynamic team. You will play a key role in building data platform from the ground up, creating the infrastructure, pipelines, and real-time processing systems that will power our products and business insights.

This is a unique opportunity to shape both the company’s data architecture and its data-driven culture.

Responsibilities

Design, build, and maintain scalable data pipelines and cloud-based data infrastructure in AWS.

Build systems capable of processing and analyzing petabytes of data in distributed production environments.

Own and evolve the analytical data warehouse on ClickHouse — schema design, ingestion pipelines from Kafka/Kinesis/S3, materialized views, query performance, and cost control at large scale.

Develop real-time stream processing solutions using Kafka, Kinesis, and Spark.

Design and manage distributed messaging and queue-based systems (SQS, RabbitMQ, etc.).

Collaborate with engineering, product, and business teams to deliver impactful data solutions and actionable insights.

Drive a data-first culture by promoting best practices and enabling smarter, data-driven decision-making across the company.

Develop backend services and integrations using TypeScript and modern cloud-native technologies.

Improve data reliability, observability, scalability, and operational efficiency.

Participate in architecture and strategic technology discussions that shape the future of our platform.

Requirements

• 4+ years of experience in Data Engineering, preferably in start-up companies.
• Hands-on experience with ClickHouse (or comparable columnar OLAP engine)
• Hands-on experience with stream processing technologies (Kafka, Kinesis, Spark).
• Hands-on experience with queues and messaging systems (SQS, RabbitMQ, etc.).
• Strong SQL knowledge and experience designing and optimizing complex queries and data models – mandatory.
• Strong hands-on experience with TypeScript.
• Experience building and maintaining large-scale distributed systems and data pipelines.
• Experience with AWS cloud infrastructure and modern CI/CD workflows.
• Familiarity with relational databases such as MySQL.
• Strong problem-solving skills, ownership mindset, and a “can do” attitude.
• Detail-oriented with a strong commitment to quality.

• Project Description:

  We are seeking a highly skilled Snowflake Data Engineer with 7 years of IT experience to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

• Responsibilities:

  • Design and develop data pipelines using Snowflake and Snow pipe for real-time and batch ingestion.
  • Implement CI/CD pipelines in Azure DevOps for seamless deployment of data solutions.
  • Automate DBT jobs to streamline transformations and ensure reliable data workflows.
  • Apply data modeling techniques including OLTP, OLAP, and Data Vault 2.0 methodologies to design scalable architectures.
  • Document data models, processes, and workflows clearly for future reference and knowledge sharing.
  • Build data tests, unit tests, and mock data frameworks to validate and maintain reliability of data solutions.
  • Develop Streamlit applications integrated with Snowflake to deliver interactive dashboards and self-service analytics.
  • Integrate SAP data sources into Snowflake pipelines for enterprise reporting and analytics.
  • Leverage SQL expertise for complex queries, transformations, and performance optimization.
  • Integrate cloud services across AWS, Azure, and GCP to support multi-cloud data strategies.
  • Develop Python scripts for ETL/ELT processes, automation, and data quality checks.
  • Implement infrastructure-as-code solutions using Terraform for scalable and automated cloud deployments.
  • Manage RBAC and enforce data governance policies to ensure compliance and secure data access.
  • Collaborate with cross-functional teams including business analysts, and business stakeholders to deliver reliable data solutions.

• Mandatory Skills Description:

  • Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
  • Hands-on experience with Python , SQL , Jinja , JavaScript for data engineering tasks.
  • CI/CD expertise using Azure DevOps (build, release, version control).
  • Experience automating DBT jobs for data transformations.
  • Experience building Streamlit applications with Snowflake integration.
  • Cloud services knowledge across AWS (S3, Lambda, Glue), Azure (Data Factory, Synapse), and GCP (BigQuery, Pub/Sub).

• Nice-to-Have Skills Description:

  - Cloud certifications is a plus

• Languages:
  • English: B2 Upper Intermediate

We are looking for a Senior Data Engineer to join our growing team. In this role, you will help build and scale modern data infrastructure and pipelines that power analytics, business intelligence, product insights, and operational decision-making across our omni-channel commerce platform.
 

Responsibilities

• Design, build, and maintain scalable, reliable data pipelines and infrastructure across cloud environments.
• Develop and manage ETL/ELT workflows to ingest, transform, and load data from multiple sources into data warehouses and data lakes.
• Partner closely with product, engineering, analytics, and business teams to ensure trusted, accessible, and timely data across the organization.
• Optimize data storage, modeling, and query performance to support reporting, analytics, and operational use cases.
• Build and maintain batch and real-time data processing systems using modern tools and frameworks.
• Improve data reliability and quality by implementing monitoring, validation, alerting, and observability practices.
• Support deployment, scalability, and orchestration of data workloads using containerization and workflow automation tools.
• Implement and maintain CI/CD pipelines for data workflows and infrastructure.
• Ensure data governance, privacy, and security standards are upheld across all data systems.
• Contribute to architecture decisions, code reviews, documentation, and best practices for data engineering.
• Stay current with emerging technologies and best practices in data engineering to continuously improve platform capabilities.

Requirements

• Excellent communication skills, including fluent verbal English, with the ability to explain technical concepts to non-technical stakeholders and collaborate effectively across teams.
• Proven experience as a Data Engineer, with strong hands-on experience building and managing production-grade data pipelines.
• Strong proficiency with cloud platforms such as AWS and/or Azure, including modern data services.
• Experience with Python and strong SQL skills for data transformation, automation, and analysis.
• Experience with distributed data processing and streaming technologies such as Apache Spark, Kafka, or similar tools.
• Familiarity with workflow orchestration tools such as Airflow, DBT, or equivalent.
• Experience with data warehousing and lakehouse technologies such as Snowflake, Redshift, BigQuery, Databricks, or similar platforms.
• Solid understanding of ETL/ELT design patterns, data modeling, and data architecture best practices.
• Experience with Docker, Kubernetes, or other containerization and orchestration technologies.
• Familiarity with relational and NoSQL databases such as PostgreSQL, MongoDB, or Cassandra.
• Experience with version control systems such as Git.
• Strong problem-solving skills and a proactive approach to performance optimization, troubleshooting, and continuous improvement.

Preferred Qualifications

• Experience with data governance, security, and privacy best practices in cloud environments.
• Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
• Experience supporting analytics, machine learning, or reporting use cases with well-structured data systems.
• Knowledge of BI and visualization tools such as Power BI or Tableau.
• Previous experience working in agile, cross-functional product and engineering teams.
• Experience in retail, e-commerce, omni-channel commerce, or POS platforms is a strong plus.

Why PredictSpring?

• Join a category-leading company transforming the future of retail and omni-channel commerce.
• Work with innovative technology powering seamless online and in-store customer experiences.
• Collaborate with a talented, global team in a flexible remote work environment.
• Enjoy opportunities for professional growth and career development.
• Make a direct impact on products used by some of the world’s most recognized retail brands.
• Be part of a fast-moving, collaborative, and customer-focused culture at the forefront of commerce innovation.

DUTIES AND RESPONSIBILITIES
- Design, build, and maintain ETL/ELT pipelines to collect, process end-to-end, and store financial data
- Write optimized, efficient code to process and analyze financial data
- Design and evolve the database schema; write and tune queries
- Build and maintain integrations with external data sources and APIs (market data feeds, vendor APIs, cloud storage)
- Identify and resolve performance bottlenecks
REQUIRED QUALIFICATIONS AND EXPERIENCE
- 1–2+ years designing and maintaining data pipelines, databases (SQL / PostgreSQL), and ETL processes
- Python with Pandas / NumPy and Git
- Comfortable working with cloud object storage — AWS S3 or GCP Cloud Storage (GCS)
- A solid understanding of or interest in financial data, instruments, and markets
- Intermediate English
NICE TO HAVE — BONUS, NOT EXPECTED
- Workflow orchestration: Airflow, Dagster, or Prefect
- Columnar / interchange formats: Parquet, Arrow, DuckDB
- Streaming or real-time feeds: Kafka, WebSocket market data
- Financial protocols / vendor APIs: FIX, Polygon, Refinitiv, Bloomberg
- Time-series databases: TimescaleDB, ClickHouse
- dbt, Docker, CI/CD
- Model Context Protocol (MCP) for AI/agent integrations
- Familiarity with R
- Background in Computer Science, Physics, or Applied Mathematics
WORK ENVIRONMENT
- Remote
- Flexible hours, 8 am – 8 pm
- Compassionate teammates and real growth opportunities
SALARY AND BENEFITS
- Paid vacation and state holidays (available to request after 8 months)
- Workshops and skills-boost courses encouraged

About us:
Gismart is a value-driven mobile app developer with a strong presence in the Health & Wellness, Utilities, and Music app markets. We have recently achieved a significant milestone of over 1 billion downloads worldwide, taking a step toward our mission of cultivating the well-being of people worldwide. With headquarters in London, UK, Gismart is a dynamic global company with a reach extending across Europe and far beyond. Our determined team consists of 250 individuals who bring diversity, creativity, innovation, and relentless drive to the company. Gismart unites professionals from diverse backgrounds in entertainment, music, and tech, allowing us to play to each other’s strengths and succeed as a team.
 

Our mission:
Become a stepping stone on our customers’ journeys of self-improvement.
 

Our values:

• Deliver what matters
• Respect and support others
• Initiate and own
• Voiceopenly
• Execute with quality
   

If you are passionate about mobile app development and want to join a company that’s reshaping the industry, Gismart is the place for you. We provide exciting career opportunities, a supportive workplace culture, and the chance to contribute to a team that is making a tangible impact. For more information on Gismart, visit gismart.com.
 

About project

FitMe is a fitness app created for people who want to stay active without long, repetitive workouts. It offers short, easy-to-follow routines that can be done at home with no equipment. FitMe is perfect for busy people who want to maintain their health, boost energy levels, and feel good every day — without going to the gym or spending hours on exercise.
 

What you will do:

• Stakeholder support: ad-hoc validation of figures, investigation of discrepancies between dashboards, ensuring availability of the BI platform.
• Development and support of ETL/ELT pipelines for analytical reports (cohort revenue, LTV, retention, KPI by creatives, post-purchase analytics).
• Support for attribution models (Web↔Mobile mapping, multi-locale cost-cohort resolve, last-click vs raw events).
• Integrations with external sources: Meta Marketing API, AppsFlyer, AppMetrica, Stripe, Tableau Server (TSC), Birch.
• Data quality assurance: schema/freshness/volume checks, broken-snapshot detection, anti-bot-traffic filtering.
• Performance optimization of queries and procedures in Redshift.
• Collaboration with BI Team, Product / UA / Billing analysts.
   

Key qualifications:

• 1-2 years of commercial experience in data engineering / analytics engineering / BI development.
• Experience with data warehouse, data lake and data lakehouse concepts.
• Proven experience in designing, implementing and supporting ETL/ELT pipelines.
• Experience with AWS, Amazon Redshift, Apache Airflow.
• Advanced SQL, hands-on experience in designing production-level Python code.
• Willingness to deal with business logic (subscription, monetization, attribution) — with team support.
   

Would be a plus:

• Experience with Infrastructure as Code (Terraform).
• Experience with dbt, Apache Spark, Apache Iceberg
• Experience with UA-attribution stack (AppsFlyer, AppMetrica, Adjust, SKAN, GA4) and UA cost APIs (Meta, Google Ads, TikTok, AppLovin).
   

Employee Benefits:

• Remote-First Culture: Our team is diverse and extensive, just like our product portfolio. We provide a flexible working schedule and let you work anywhere in the world, either remotely or in one of our corporate hubs.
• Сoworking Compensation: We provide 100% compensation for coworking spaces in Ukraine (except Kyiv, where we have a cozy and autonomous hub) and 50% compensation for coworking spaces worldwide to make sure that everyone has a comfortable and inspiring workspace.
• Free Office Lunches: We want you to stay fueled and focused, so we provide delicious free lunches for everyone working from our hubs. It’s one less thing for you to worry about during a productive workday.
• Public Holidays Policy: Gismart observes public holidays based on the Ukrainian or Polish holiday calendars. Employees working in other locations are entitled to 12 public holidays per year.
• 100% Sick Leave Compensation.
• Mental and Physical Health: To help you stay happy and healthy, Gismart will partially cover your medical insurance, sports activities, therapy costs.
• Personal Equipment Policy: Of course, we’ll provide you with all the equipment you need or cover the maintenance cost for your own tools.
• Personal Learning and Development: We love to see you learn and grow, so Gismart will cover 70% of your professional development courses. We’ll also pay for 100% of corporate English courses.
• Time-Off Policy: Every employee gets 18 working days of paid vacation and 3 personal days per year, with an extra day added for each year at Gismart.
• Corporate Events: We are remote-first, but we love meeting up in person. Whenever possible, we organize corporate events and team-building activities across all Gismart hubs.

At Geniusee, we help businesses thrive through technology partnerships and strengthen the engineering community by sharing knowledge and creating opportunities 🙌

We live by our values: Continuous Growth, Team Synergy, Taking Responsibility, Conscious Openness, and Result-Driven Mindset. Here, you’ll find a safe, inclusive, and supportive environment where your voice is heard, and feedback is welcome 🤗

Whether you want to work from home or from our offices in Kyiv / Lviv / Warsaw with stable electricity and Wi-Fi, we’ve got you covered.
Join us and make an impact 💜

About the project:
FinTech

 

REQUIREMENTS:
● 3+ years of experience in Data Engineering
● Strong data engineering background with the ability to design and own solutions end-to-end
● Proficiency in Python, Airflow, dbt, and Redshift for data processing, pipeline development, and transformation
● Experience building and maintaining ETL / ELT pipelines and data integrations, including fetching and normalizing data from non-robust 3rd party sources
● Hands-on experience with LLM/agent-based automation applied to business processes (e.g., building agents or LLM-powered workflows for data transformation, testing, or extraction)
● Practical familiarity with modern AI tooling — LLM APIs (OpenAI, Anthropic, etc.), RAG patterns, prompt engineering, and agent frameworks (LangChain, LlamaIndex, or similar)
● Cross-functional flexibility: comfortable stepping beyond pure DE work into adjacent areas — light DevOps (Docker, CI/CD, cloud deployment), backend integration, and basic frontend when a POC requires it
● Excellent communication skills — able to explain technical decisions to non-technical stakeholders
● Self-directed and proactive: able to spot workflow inefficiencies and drive improvements with minimal supervision
● Product thinking: collaborate with business teams, propose solution approaches, build quick POCs, iterate on feedback, and support production deployment

RESPONSIBILITIES:
● Analyze business workflows and identify opportunities for data automation and AI-driven process automation
● Design, build, and maintain scalable data pipelines and integrations, including ingestion from unreliable or unstructured 3rd party sources
● Build LLM- and agent-based solutions for data transformation, validation/testing, and extraction tasks
● Containerize data and AI workloads using Docker and deploy to cloud infrastructure (AWS)
● Develop prototypes and POCs to validate ideas quickly — both data pipelines and AI-powered workflows
● Collaborate with business and technical teams to refine requirements and iterate on solutions
● Support the deployment and integration of data and AI solutions into production systems
● Continuously improve data processes through automation and AI-driven approaches
● Contribute to data modeling, quality, and observability practices

Preferred Experience:
● Experience combining traditional ETL/data pipelines with LLM/agent components in production or near-production settings
● Experience working in cross-functional product teams
● Familiarity with cloud-based data platforms (AWS preferred)
● Experience with vector stores, embeddings, or retrieval-augmented generation (RAG)

What will you get:

Career growth & projects
● Competitive salary based on your skills and experience, and a benefits package.
● Regular performance appraisals to support your career development.
● Professional development support: online courses, certifications, and study compensation (including English courses).
● A modern tech stack across challenging, long-term projects.

Health & well-being
● Paid vacation (18 working days) and sick leave (15 days).
● VIP medical insurance or sports coverage.

Work environment & flexibility
● Remote work from anywhere in the world or in one of our offices in Kyiv, Lviv, or Warsaw.
● Coworking compensation.
● Necessary equipment to perform your work.

Culture & community
● A collaborative and open culture where contributions are recognized.
● Regular corporate online & offline activities.
● Geniusee Charity Fund, making a difference beyond tech.

Summary

# Job Title

Product-Minded Full-Stack / AI Engineer for Web Data Intelligence Platform

# Job Description

We are building an AI-powered platform that collects, structures, and analyzes information from public websites and documents.

The system needs to process messy real-world data from many organizations, extract useful structured information, store it in a database, and present it through a user-facing dashboard.

This is not a basic scraping project. We are looking for someone who can think through system architecture, data pipelines, database structure, AI extraction workflows, and product design.

The right person should be able to take an existing MVP, understand the logic behind it, and help redesign it into a more scalable and configurable system.

# What You’ll Work On

You will help design and build a platform that can:

* Discover and process public web pages and documents.
* Extract structured information from unstructured text and PDFs.
* Normalize information into a database.
* Track updates over time.
* Detect duplicates and changes.
* Classify and score records based on configurable rules.
* Match structured records to user-defined profiles.
* Support a dashboard for review, filtering, and workflow management.
* Make the system configurable across different use cases.

# Required Skills

Strong fit if you have experience with:

* Python and/or Node.js
* Web crawling / scraping
* PDF and document extraction
* LLM-based extraction and classification
* Database design
* Full-stack development
* Data pipelines
* AI workflow design
* Prompt engineering
* Handling messy real-world data

Bonus if you have worked with:

* Public web data
* Search or monitoring systems
* Workflow dashboards
* Vector databases
* Supabase / Postgres
* Playwright
* OpenAI / Claude APIs
* LangChain / LlamaIndex or similar tools

# Important

We are not looking for someone who only knows how to scrape websites.

We are looking for someone who can help turn an MVP into a structured, scalable intelligence platform.

You should be comfortable thinking about:

* Data models
* Extraction quality
* Source reliability
* Change detection
* Deduplication
* Configurable scoring logic
* Human review workflows
* Product usability

# Initial Paid Test Project

The first step will be a paid test project.

You will receive limited sample data, example outputs, and a simplified description of the existing MVP.

Your task will be to:

1. Understand the current MVP structure.
2. Propose a more scalable system architecture.
3. Propose a database schema.
4. Define how configurable extraction and scoring should work.
5. Build a small prototype that processes a few public URLs or documents and produces structured output.

The prototype does not need to be production-ready. We care more about your thinking, structure, and ability to generalize the system.

# Test Project Deliverables

Please deliver:

* Short design document.
* Simple architecture diagram.
* Database schema proposal.
* Configuration example.
* Working prototype code.
* Example structured output.
* Notes on what you would build next.

# Example Output Fields

The system should produce structured records with fields such as:

* Source organization
* Record title
* Record description
* Category
* Status or stage
* Date / timeline if available
* Source URL or document
* Evidence from source
* Confidence score
* Match score against a sample profile
* Reason for match
* Suggested next action

# What We Are Looking For

A strong candidate will think in terms of:

* Sources
* Documents
* Records
* Evidence
* Configurations
* Profiles
* Matches
* Processing runs
* Review workflows
* Data quality
* Deduplication
* Change tracking
* Explainable scoring

A weak candidate will only talk about scraping pages.

# Engagement

This will start as a paid test project. If successful, it can become an ongoing engineering role.

We are looking for someone who can eventually take ownership of the platform architecture and development.

# To Apply

Please answer the following questions:

1. Describe a similar system you have built or would know how to build.
2. How would you separate use-case-specific logic from the core platform?
3. How would you detect whether a record is new, updated, duplicate, stale, or no longer relevant?
4. What database schema would you start with?
5. What would you automate, and where would you keep human review?
6. Please share examples of relevant work if available.

Applications without thoughtful answers will not be considered.

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an Senior Verification Engineer specialist to join one of our clients' teams that develops RISC-V CPU cores. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

As a Senior Verification Engineer, you will work within the Verification Team and play a crucial role in ensuring the correctness and functionality of complex digital designs at the Register Transfer Level according to the specification, using advanced verification methodologies and tools to validate the designs.

Requirements:

• Master or PhD
• English C1
• Industrial experience +8 years
• Proficiency in SystemVerilog and UVM
• Knowledge of scripting languages (Python, Perl, Bash, TCL) and regression tools 
• Experience with simulation and simulation tools
• Knowledge of revision control methodology and tools (git, svn)
• Experience in block level and sub-system or top level verification
• Experience with formal and dynamic verification
• Strong problem-solving skills and attention to detail
• Excellent communication and teamwork abilities

Digis is looking for a Data Engineer to join our long-term partnership with a US-based global leader in hospitality analytics.

About the Project
Our client is a premier SaaS provider that transforms front-desk staff into revenue generators for the world’s most iconic hotel brands, including Hilton and Marriott. Their platform operates in over 2,000 properties across 124 countries, managing billions in revenue.

The project is in an active development stage, focusing on processing massive data volumes to deliver real-time business value.
You will join a large, distributed team (approx. 50 people) and collaborate directly with the US-based CTO.

Your Responsibilities:

• Manage data pipelines from ingestion to ready-to-use analytical models.
• Help scale case detection processes and improve existing services by splitting them into smaller microservices.
• Build, debug, and optimize ETL jobs using AWS Glue, EMR, and Spark.
• Write and improve SQL queries for PostgreSQL/Redshift, and work with DynamoDB where needed.

• Support workflows in AWS ECS Fargate and set up local environments for testing and deployment.

   

Requirements:

• 3+ years of experience as a Data Engineer.
• 1+ years of hands-on production experience with Spark or PySpark.
• 1+ years of experience with the AWS ecosystem (specifically Glue, EMR, S3, and Redshift).

• English: High level (Upper-Intermediate/Advanced) for frequent technical meetings and direct collaboration with the US CTO.

   

We Offer

• 20 paid vacation days per year
• 5 paid sick leaves per year (no medical documents required)
• Personalized development plan with training compensation
• English courses compensation
• Work equipment if needed (PC / laptop / monitor)
• Flat and transparent internal communication
• Opportunity to switch between projects and technologies within Digis
• Full accounting and legal support

DevOps Engineer Requirements

Core Skills:

✅ 3+ years of experience in DevOps or SRE roles

✅ Strong command of Linux server administration and shell scripting

✅ Proven experience with Docker containerization and optimization

✅ CI/CD pipeline implementation experience (GitHub Actions, Jenkins, or similar)

✅ Infrastructure as Code experience (Terraform, Ansible, or similar)

Technology-Specific Requirements

1. Node.js/React:

✅ Experience deploying and maintaining Node.js applications

✅ Familiarity with React build processes and optimization

JavaScript/TypeScript ecosystem knowledge

2. Docker:

✅ Container orchestration (Docker Compose, Docker Swarm, Kubernetes, or similar)

✅ Multi-stage builds and image optimization

✅ Container security best practices

MongoDB:

✅ Cloud MongoDB management (Atlas or similar)

✅ Database backup/restore procedures

✅ Performance monitoring and optimization

Cloudflare:

✅ CDN configuration and optimization

✅ DNS management

✅ WAF and security rules implementation

OS Administration:

✅ Linux server management (Ubuntu/Debian preferred)

✅ Basic Windows server administration

✅ Cross-platform automation skills

✅ Additional Qualifications

✅ Experience with cloud providers (AWS, Azure, GCP)

✅ Monitoring and observability implementation (Zabbix, Grafana, Portainer, or similar)

✅ Security best practices and vulnerability management

✅ Incident response and troubleshooting skills

✅ Automated testing integration in deployment pipelines

Description

About the Client

Our client is a neocloud purpose-built for AI workloads. We design, deploy, and operate GPU clusters at scale, bare-metal compute, high-performance networking (NVLink, InfiniBand, RoCE), and the software platforms that make them usable for training and inference customers. We move fast, we own our infrastructure end-to-end, and we are building the operational foundation for the next generation of AI compute.

The Role

We are hiring a Principal Production Operations Engineer to serve as part of the senior technical backbone of how we run production. This is a generalist role. You will operate horizontally across systems administration, DevOps, SRE, automation, platform engineering, and vertically from hands-on incident command to multi-quarter architectural strategy and execution.

This is not a people-manager role. It is a senior individual contributor role with org-wide technical influence. You will help set the standards that the rest of the operations org follows, lead the hardest incidents, design the automation and platform investments that determine how we scale, and raise the bar for every engineer around you. We expect you to have already done this work somewhere else, owned production at scale, survived the outages, built the systems that prevented the next ones, and mentored the engineers who now run them.

You will work alongside our network architects, platform engineers, and datacenter delivery team. You will be expected to have strong opinions, defend them with data, and change your mind when the evidence says you should.

What you'll own

Production infrastructure and reliability

• End-to-end ownership of reliability, performance, and operational health across the company's production infrastructure, bare-metal GPU clusters, OpenStack, Kubernetes, storage, and the networking and security layers underneath.
• Technical leadership during major incidents: incident command, root cause analysis, and the durable fixes that ensure the same class of failure does not recur.
• Architectural decisions for how we scale compute, storage, and networking across

multiple datacenters and cluster generations.

• Lifecycle workflows for bare-metal provisioning, node onboarding, decommissioning,

firmware management, and fleet-wide remediation.

• Capacity planning, performance engineering, and the operational readiness reviews that gate new clusters into production.
   

Automation and platform engineering

• Set the bar for infrastructure-as-code, configuration management, and operational

tooling across the org. Define the patterns; do not just follow them.

• Design and build the automation that eliminates classes of toil, deployment, scaling,

failover, provisioning, firmware, observability, security posture.

• Own the CI/CD, GitOps, and release engineering primitives that production runs on. Make them boring, reliable, and self-service.
• Choose the right tools and frameworks (Terraform, Ansible, Helm, Python, Go, Bash) for the problem in front of us, and know when to build versus buy versus avoid.
• Drive the AI-assisted operations strategy, agentic runbooks, MCP-integrated tooling, and the workflows that let a small team operate a very large fleet.
   

Observability and operational discipline

• Evolve the observability stack (OpenTelemetry, Prometheus, Grafana, Alertmanager,

Loki, Checkmk) into a platform engineers trust.

• Define what good looks like for instrumentation, SLOs, alerting, and on-call hygiene.

Drive the org toward signal over noise.

• Lead post-incident reviews. Translate findings into concrete engineering work, not action items that die in a doc.
• Participate in the on-call rotation.
   

Technical leadership and force multiplication

• Mentor senior and mid-level engineers. Raise the technical ceiling of the team without

becoming the bottleneck.

• Write the design docs, the runbooks, and the standards that the rest of the org builds on.
• Prioritize engineering effort against real operational risk. Say no to the work that does

not matter; say yes loudly to the work that does.

• Partner with the leadership on org-level technical strategy.
• Represent operations in customer-facing technical conversations when the situation calls for it.
   

What we're looking for

Required

• 10+ years in Operations, DevOps, SRE, Production Engineering, or Infrastructure

Engineering, with significant time at the senior or staff level.

• Deep Linux internals knowledge.
• Proven track record owning production systems end-to-end at scale, not just

participating in operations, but defining how operations work.

• Strong programming and automation skills in at least one of Python, Go, or similar. Bash fluency is assumed.
• Hands-on expertise with Terraform, Ansible, or equivalent configuration management at scale.
• Hands-on expertise with modern observability stacks (Prometheus, Grafana, Loki,

OpenTelemetry) instrumentation, SLO definition, alert design, and reducing alert fatigue.

• Networking depth well beyond fundamentals: routing, NAT, conntrack behavior, load

balancing, DNS, TLS, and the ability to troubleshoot when needed.

• Experience leading incident response and post-incident review for high-severity

production events.

• Track record of mentoring engineers and elevating the technical standards of a team or org.
• Comfortable working in ambiguity. You build a structure where there is none and do not

wait to be told what to do.
 

Strongly preferred

• Production OpenStack experience, particularly Ironic, Nova, Neutron, and the

operational realities of bare-metal provisioning at scale.

• Kubernetes at scale, including GPU-aware workloads, operators, and the failure modes of CNI, CSI, and the control plane.
• GPU compute or AI infrastructure experience, NVIDIA driver and Fabric Manager

troubleshooting, NCCL, InfiniBand or NVLink fabrics, CUDA version management across

fleets.

• Hardware-aware operations: firmware management, BMC/Redfish automation, BIOS

configuration at fleet scale, and the operational discipline that goes with bare metal.

• Startup or scale-up experience. You know what it feels like to build the second version of everything because the first one was duct tape.
• Experience integrating AI-assisted tooling (Claude Code, agentic workflows, MCP

servers) into production operations.
 

How we work

• We are remote-first. We rely on written communication, async-by-default decision

making, and high-bandwidth pairing when it matters.

• We own our infrastructure. We do not hand off problems to vendors and wait. We

diagnose, we fix, and we write down what we learned.

• We use AI as a force multiplier, not as a replacement for engineering judgment, and not

as a novelty. We expect every engineer to be fluent with it.

• We participate in a 24/7 on-call rotation. Principal engineers are also an escalation point when the rest of the rotation needs help.
  
   

AI as a Force Multiplier

Operations is built on the assumption that a small, senior team augmented with AI can outperform a much larger traditional ops org. We expect you to use Claude Code, MCP-integrated tooling, and agentic workflows as core parts of how you investigate, automate, and document. If you have not used these tools in production yet, you should be hungry to. If you have, you should be ready to help define how the rest of the team uses them.

What success looks like

First 30 days

• Onboarded into all production systems, and the active incident backlog. You know where the bodies are buried.
• Shadowed an incident, and contributed at least one durable fix to a recurring operational issue.
• Identified the top three operational risks you would prioritize in the next quarter and started building consensus on them.

First 90 days

• Owning at least one major reliability or automation initiative end-to-end, with measurable impact (toil reduction, MTTR improvement, or reliability gain).
• Driving meaningful improvements to incident response, observability, or platform tooling that the rest of the org has adopted.
• Actively mentoring at least one mid-level or senior engineer.

First 12 months

• Recognized across the company as a technical authority on production operations. Architecture decisions of any significance include you.
• Shipped at least one platform-level investment (automation, observability, or lifecycle) that has materially changed how we operate.
• Raised the operational maturity of the org by a visible step, measured in incident frequency, MTTR, toil, and engineer quality of life.
• Influenced the hiring bar and the technical direction of operations beyond your own scope of work

About the Company

We are a specialized consulting company focused on Statistics, Data Science, and AI. We work across multiple industries, including the public sector, insurance, and other data-intensive domains.

For this role, we are specifically looking for a Senior Frontend Engineer to join projects in the public sector. These projects involve building technically advanced solutions that also create a meaningful positive impact on society.

We are looking for experienced engineers who enjoy solving complex problems and want to build systems that make a real difference.

Role Overview

As a Senior Frontend Engineer, you will take end-to-end ownership of frontend development — from architecture and technical decision-making to prototyping, implementation, testing, quality assurance, and continuous improvement.

You will build analytical web applications that are intuitive, accessible, performant, and reliable across desktop and mobile devices.

What You'll Be Doing

• Design and own frontend architecture together with the engineering team.
• Develop modern web applications using React and JavaScript/TypeScript.
• Translate complex business and analytical requirements into intuitive user interfaces.
• Build responsive user experiences that work seamlessly across desktop and mobile devices.
• Develop interactive data visualizations and analytical UI components.
• Ensure charts, filters, tables, and interactions are both technically accurate and user-friendly.
• Implement accessibility best practices throughout the development lifecycle.
• Rapidly prototype ideas, validate concepts, and transform successful prototypes into production-ready solutions.
• Maintain high standards for code quality, testability, scalability, and maintainability.
• Define and uphold frontend engineering standards, code reviews, testing practices, and UI consistency.
• Make pragmatic technical decisions and provide leadership on frontend direction.

Requirements

• Several years of professional experience developing modern frontend applications in production environments.
• Strong expertise in React, JavaScript, and TypeScript.
• Excellent understanding of UI, UX, and visual design principles.
• Experience building responsive web applications for both desktop and mobile devices.
• Experience with component-based frontend architectures and high-performance user interfaces.
• Knowledge of accessibility standards and implementation best practices is a strong advantage.
• Experience with frontend testing and a strong focus on code quality and maintainability.
• Experience with data visualization libraries such as Plotly.js, ECharts, d3.js, or similar tools.
• Ability to design clear and intuitive analytical interfaces for presenting complex information.
• Ability to build solutions both using existing frameworks and, when necessary, from scratch.
• Strong technical judgment, structured problem-solving skills, and clear communication.

Candidate Requirements

• Ukrainian native speaker
• English proficiency: B2 or higher

Laba Group is one of the largest international EdTech companies in Eastern Europe. Currently, our team consists of over 800 colleagues united by a single mission: to improve people’s lives through relevant and systematic knowledge.

In 10 years, we have launched over 1,400 courses and educated more than 200,000 students globally. We have also created 5 online education brands:

• Laba — an online business school
• Skvot — a school of popular culture
• robot_dreams — a platform for growth in IT
• Vocabulaba — a school of English for professionals
• elvtr — an online school in the US and the UK

We are seeking a Data Engineer. The primary purpose or objective of this role is to be responsible for designing, building, and maintaining a scalable data infrastructure that supports analytics, reporting, and business decision-making within the organization.

The role focuses on developing robust data pipelines, integrating multiple data sources, ensuring data quality, and delivering business-driven data solutions that align with the company’s goals.

What will be the main responsibilities ?

• Design, build, and maintain scalable ETL/ELT pipelines.
• Integrate data from internal and external systems (CRMs, LMS, marketing platforms, databases, etc.).
• Develop and maintain API integrations (REST/JSON, OAuth, etc.).
• Automate recurring data processes using Python (or similar scripting languages).
• Design and maintain data models optimized for analytics and operational reporting.
• Work with cloud data warehouses (e.g., BigQuery, Snowflake) to ensure efficient storage and retrieval.
• Implement data validation and monitoring mechanisms to ensure accuracy and reliability.
• Proactively monitor and resolve data pipeline failures.
• Create dashboards and data applications using low-code/no-code platforms (e.g., Power BI, Retool, Coda, Tableau, Google Sheets).
• Translate complex datasets into clear, actionable insights for business stakeholders.
• Collaborate with analysts, data scientists, and business teams to define KPIs and reporting requirements.
• Suggest improvements to existing data flows and business processes.
• Maintain structured and comprehensive technical documentation (pipelines, integrations, data definitions, architecture).
• Participate in cross-functional discussions to align technical implementation with business needs.

Requirements

• Strong proficiency in Python (or similar scripting language) for data manipulation and automation.
• Advanced SQL knowledge and experience in data modeling.
• Hands-on experience with API integrations (REST, JSON, OAuth) and related tools (e.g., Postman).
• Experience working with cloud platforms (GCP, AWS) and cloud data warehouses.
• Experience with orchestration tools (e.g., Airflow, dbt, or similar frameworks).
• Familiarity with Git and CI/CD processes.
• Experience with low-code/no-code platforms for data visualization or workflow automation.
• Solid understanding of business processes and KPI-driven decision-making.
• 1–4 years of experience in a data engineering or data-focused role.
  
  Skills that would be “nice to have”:
• Experience developing and customizing internal tools using Retool, with working knowledge of JavaScript.
• Experience optimizing query performance and cost efficiency in cloud environments.
• Exposure to real-time data streaming solutions.
• Experience supporting AI/ML data pipelines.
• Strong data storytelling and stakeholder communication skills.

Benefits

Why Laba Group is the Place to Be🔥:

• Flexible full-time. We focus on results, not hours worked: manage your own time, stay connected with the team, and meet deadlines.
• Remote, onsite, or hybrid — it’s up to you! Our modern workspace in Kyiv is fully equipped with everything you need.

As part of our team, you’ll get:

• Access to all Laba Group courses for you and your family.
• Compensation for external training, sports, and mental and physical health, helping you stay balanced and prioritize your well-being.
• Unlimited vacation after 6 months of working with us. Plan according to your needs and adjust based on workload intensity because rest is essential for your health.
• We create a supportive environment where facing challenges and making mistakes isn’t scary. A place to find like-minded people and become part of a larger community. A service that truly puts the client first.
• No micromanagement or bureaucracy — only automated processes. Feel free to experiment, propose, and implement your ideas.
• We champion a proactive mindset, bold ideas, and a willingness to embrace change. Laba isn’t about going with the flow — it’s about pushing the world forward.
• We provide only up-to-date insights from market experts—knowledge that can be immediately applied in practice, transforming workflows even during the learning process. Our products are designed for decisive action in business and careers.
• A socially responsible environment: we regularly launch charity projects and fundraisers to support Ukraine and its defense forces.
• Vertical and horizontal growth: 83% of our Heads of Departments and Team Leaders have been promoted internally.

Our recruitment process is transparent and straightforward.

Three stages: an interview with the Rectuiter→ Test Task→ an interview with the HM’s team

After that, the job offer will be in your inbox.🙌

We’re not looking for perfect candidates. We’re looking for bold and “one of us” people. 💛

Join the team. It’s going to be an amazing journey.

Project description

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal assistant that uses hosted APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

Responsibilities

Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)

Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests

Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.

Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history

Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

Skills

Must have

8+ years of hands-on experience in Data Science and 2+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.

Strong programming skills in languages such as Python and familiarity building ETL pipelines.

Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases

Solid experience with OpenSearch

Familiarity with AWS cloud platform and its services.

Experience with version control systems (e.g., Git) and CI/CD pipelines.

Ability to build scalable infrastructure to embed and search very large number of documents.

Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.

Expertise in ML

Strong English skills (B2 and higher)

Strong verbal and written communication skills.

Ability to work independently and collaborate in a group.

Nice to have

Agile certification

Oracle/Microsoft attestations and certifications

Domain knowledge

Trading and Capital Markets

Languages

English: C1 Advanced