Jobs Data Engineer

We are looking for a Senior Data Engineer to join an experienced technical team working on modern data platforms and large-scale data processing.

The role combines data architecture, ETL, data warehousing, and big data / real-time pipelines, with solutions running both on-premises and in the cloud.
 

What you’ll do

• Design and implement data models for complex data environments
• Develop and maintain ETL/ELT processes
• Build and optimize big data and real-time data pipelines
• Work with data platforms across on-premises and cloud environments
• Design and implement solutions for data warehousing and data movement
• Work with relational databases and large datasets
• Optimize queries and data processing workflows
• Develop data engineering solutions using SQL and Python
• Collaborate with other engineers and technical stakeholders on data architecture and implementation
   

Requirements

• 3–5+ years of experience in Data Engineering
• Strong experience with database technologies and data architecture
• Hands-on experience with Oracle — especially relevant for this role
• Experience with data warehousing
• Experience with ETL and data movement technologies
• Excellent SQL skills
• Strong Python development skills
• Experience building big data and/or real-time data pipelines
• Understanding of both on-premises and cloud-based data environments
• Strong analytical and problem-solving skills
• Fluent English — required for daily communication
   

Education

• Master’s degree in Computer Science or a comparable technical qualification
   

What you’ll work with

• Oracle and other database technologies
• SQL
• Python
• ETL / ELT
• Data warehousing
• Big Data pipelines
• Real-time data processing
• On-premises and cloud infrastructure
   

What We Offer

• Paid vacation and sick leave
• Equipment to work comfortably, if required
• 50% coverage of professional development courses
• Regular salary reviews based on performance
• Competitive salary, depending on experience
• Always on-time payments — no delays
• A supportive team without unnecessary bureaucracy

About the team
We are a focused engineering team of 5 to 9 people at a multi-product tech SaaS company. We work across personalization, recommendation, fraud detection, and some GenAI, through real-time, near-real-time, and batch pipelines. The team is a blend of Python backend engineers, ML engineers, QA (manual and automation), a data engineer, and data scientists. Our shared mandate is to take experimentation from our data scientists and turn it into
robust, production-grade systems, either as standalone services or integrated into wider platforms.

The role
You will build the data pipelines that power our experimentation and production systems, the backbone behind fraud detection, recommendations, and personalization. You will work primarily with Kafka (sometimes Flink), Spark on Databricks, and Airflow, modelling and optimizing data for both experimentation and production serving, and partnering closely with data scientists and backend engineers on data contracts and reliability.

Responsibilities
• Build and operate batch and streaming data pipelines (structured & unstructured data)
• Work primarily with Kafka, Spark on Databricks, and Airflow
• Model and optimize data for both experimentation and production serving
• Partner with data scientists and backend engineers on data contracts and reliability
• Own pipeline quality: testing, monitoring, and performance
• Take part in the teams on-call rotation for the pipelines you build and operate

Requirements
• 3+ years of data engineering or software engineering for data

• Strong Python, including PySpark
• Kafka and stream processing
• Databricks experience
• Airflow or similar orchestration tools
• Strong SQL and data modeling, plus solid relational database skills
• Software engineering mindset
• Fluent spoken English, as you’ll work with an international team and all interviews will be conducted in English.

Nice to have
• Flink

• ML Feature stores
• MLOps and ML pipeline exposure
• Docker and Kubernetes
• SaaS or high-scale product domain experience

Why join us
• Work on high-scale, high-throughput systems
• Broad range of technologies, stacks, and problems
• Contribute to turning experimentation into production systems
• A strong engineering culture that values technical depth and ownership
• Flexibility through hybrid and remote working
• A distributed team across Dubai and Europe
 

You will get

• A position in a product development company with a dynamic environment and several concurrent projects;
• Continuous self-improvement and growth, including budget for certifications and courses;
• Competitive salary plus financial bonuses;
• Medical insurance coverage;
• Company prepaid AI agent;
• Wellbeing package: online-yoga classes, Yakaboo, BetterMe App: Health Coaching, BetterMe App: Mental Health;
• Corporate events and fun team-building activities.
• Remote-first culture
   

Interview Stages

1. HR Interview (30-45 minutes) — Initial conversation to discuss your experience, career goals, and cultural fit.
2. Technical Interview (1 hour) — In-depth technical interview covering relevant skills.
3. Final Interview (1,5 hour) — A comprehensive discussion with the team, focusing on role-specific competencies and alignment with company values.
4. Reference check & Job Offer

Hey there!

It’s an amazing time to join Digicode as we continue empowering as many innovators as possible to change the world. We deliver outstanding technology consulting, R&D, design, and software development services in the USA, UK, and EU markets. This is where you come in.

We are looking for an Azure Data Architect to own the design and modernization of data platforms that support production GenAI, retrieval-augmented generation (RAG), and agentic AI applications. You will shape AI-ready data architecture, make key design decisions, and work with delivery teams to turn those decisions into reliable solutions.

Requirements:

• Hands-on experience with Azure Cosmos DB.
• Experience designing data platforms that support GenAI or RAG use cases.
• Experience designing AI-ready data architecture.
• A real project in which you designed or contributed to the data architecture supporting a RAG, GenAI, or agentic AI application.
• Strong architecture ownership and the ability to explain and defend design decisions.
• Experience working with both structured and genuinely unstructured data..

Responsibilities:

• Design Azure data platforms and lakehouse architectures for analytics and AI use cases.
• Define how structured and unstructured data is ingested, modeled, governed, and made available to RAG and GenAI applications.
• Own architectural decisions across data storage, processing, integration, and metadata.
• Lead data modeling and data-platform modernization initiatives.
• Collaborate with engineering and AI teams to deliver architecture that works in real applications.

Relevant technical background:

Experience across Azure Synapse Analytics, Azure Databricks, Azure SQL or SQL Server, Azure Data Lake, Azure Functions, lakehouse architecture, data modeling, metadata, and data-platform design and modernization.

What To Expect From Digicode?

• 🌎 Work from Anywhere: From an office, home, or travel continuously if that’s your thing. Everything we do is online. You’re all set as long as you’re in the same time zone as your project colleagues.
• 💼 Professional Development: We offer great career development opportunities in a growing company, international work environment, paid language classes, conference and education budget, & internal 42 Community training.
• 󰩔 Work-life Balance: We provide employees with 18+ paid vacation days and paid sick leave, a flexible schedule, medical insurance for employees and their children, and a monthly budget for things like a gym or pool membership.
• 🙌 Culture of Openness: We’re committed to fostering a community where everyone feels welcome, seen, and heard, with minimal bureaucracy, and flat organization structure.
  And, also, corporate gifts, corporate celebrations, free food & snacks, and play & relax rooms for those who visit the office.
• Our culture is unique, and we live by our beliefs and core values. For more information on what it’s like to work at Digicode, please take a look at our career page.
• We offer official type of employment via GIG-contract.

Did we attract your attention? Send your CV and let’s talk:)

*By submitting your resume, you automatically agree to the storage and processing of your personal data in accordance with the current legislation of Ukraine. We guarantee confidentiality at all stages of cooperation.

N-iX is looking for Senior DataOps Engineer to join the team.

Client Overview:

Our client is an Azerbaijani telecommunications company, the largest mobile network operator in Azerbaijan. The main products are: Fixed telephony, Mobile telephony, Internet services, Wireless broadband, and Value-added services.

Project Objectives:

The primary goal is to accelerate the client’s Data & AI initiatives via a secure, hybrid cloud foundation on AWS while systematically modernizing the IT estate as part of the cloud migration.

Key Project Objectives include:

• Cloud Foundation & Landing Zone: Deploy target hybrid network architectures, establishing a secure Landing Zone and hybrid Data/AI platforms on AWS.
• Security, Compliance & Governance: Operationalize on-prem tokenization (achieving zero raw PII in the cloud), resolve policy blockers to include AWS in the ISMS, and establish a Cloud Center of Excellence (CCoE) to govern Cloud adoption.
• AI Chatbot & Voicebot Design & Implementation: Develop and operationalize a flagship Customer Care Chatbot and Voicebot as the first hybrid-setup consumer.

Key Responsibilities:

• Design, implement, and maintain the AWS Data Platform foundation, including Amazon S3 lake layout, Apache Iceberg table format standardization, and AWS Glue Data Catalog integration.
• Build and optimize scalable batch and streaming data pipelines using Amazon EMR (Serverless and EMR-on-EKS), Apache Spark/PySpark, Apache Flink, and Amazon Athena with workgroup cost caps.
• Implement data tokenization and de-identification pipelines on the data side (PA-T) using Protegrity/Spark UDFs for batch, Spark Streaming for micro-batch, and Kafka Connect SMT for streaming PII masking prior to cloud transit.
• Build and manage real-time streaming architectures with Amazon MSK (Managed Streaming for Apache Kafka), MSK Replicator/MirrorMaker2, and Schema Registry integration.
• Implement Medallion Architecture (Bronze, Silver, Gold layers) for lakehouse data modeling, automating Iceberg table registration and backfill frameworks.
• Execute data migration waves across non-PII and PII datasets using AWS DataSync, automated register steps, and streaming migration configs.
• Configure data access control and governance models using AWS Lake Formation, cross-engine authorization, AWS Macie for PII detection, and Informatica Data Catalog (Axon, EDC, BDQ) integration.
• Build automated schema-drift identification components, GitLab CI/CD pipeline integration for dataset synchronization, and automated data contracts/circuit breakers.
• Implement DataOps observability, centralizing logging via Amazon CloudWatch, configuring FinOps cost & anomaly monitoring, and setting up automated alerts.
• Author technical documentation, operational runbooks, disaster recovery (DR) procedures, and cutover/rollback playbooks for data platform hardening.

Requirements:

Mandatory Technical Skills:

• 4+ years of hands-on experience as a Data Engineer or DataOps Engineer building enterprise-grade data platforms and pipelines.
• Strong expertise with AWS Data Analytics stack: Amazon S3, AWS Glue Data Catalog, AWS Lake Formation, Amazon EMR (EMR-on-EKS / Serverless), Amazon Athena, AWS DataSync, and Amazon MSK.
• Deep experience with Apache Iceberg table format, cataloging, compaction, and schema evolution.
• Proficient in Apache Spark / PySpark and Spark Streaming for batch, micro-batch, and real-time data processing.
• Strong experience in Medallion Lakehouse Architecture design and implementation (Bronze, Silver, Gold layers).
• Practical experience in implementing data tokenization and encryption at scale (e.g., Protegrity, Thales, FPE, or Spark UDF-based de-identification pipelines).
• Solid knowledge of event-driven architectures & streaming: Apache Kafka / Amazon MSK, Kafka Connect (SMT), Schema Registry, and MirrorMaker2 / MSK Replicator.
• Expertise with Relational Databases (Amazon RDS, PostgreSQL, Oracle) and data synchronization techniques.
• Hands-on experience with DataOps CI/CD & Automation: GitLab CI/CD, Infrastructure-as-Code (Terraform / AWS CDK), schema-drift detection, and data contract validation.
• Familiarity with data governance tools and enterprise data catalogs (e.g., Informatica Axon/EDC, AWS Lake Formation).

Strong Plus (Nice-to-Have Skills):

• AWS Certified Data Analytics – Specialty or AWS Certified Data Engineer – Associate.
• Experience in telecom domain data models, CDR processing, and high-throughput real-time telemetry.
• Experience with cloud-side tokenization/detokenization via Athena UDFs / AWS Lambda.
• Familiarity with containerization (Docker, EKS, Kubernetes) for big data runtimes.
• Experience with AWS Macie and FinOps cost-allocation/anomaly-detection frameworks.

Soft Skills & Team Fit:

• Strong critical thinking, problem-solving, and analytical skills.
• Excellent communication and collaboration skills to work closely with cross-functional teams (Data Science, Cloud/Platform, Security, Governance).
• Results-oriented, proactive mindset with strong ownership of deliverables within an Agile / Scrum framework.
• Upper-Intermediate+ English level (written and spoken).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*benefits are not applicable for freelancers

Join the Dataforest Team!

We are actively expanding our team and looking for strong new talent to join our ranks. If you are a Data Engineer seeking a friendly team, a healthy working environment, and a flexible schedule — you have found the right place to send your CV!

You'll be a great fit if you have:

- 2+ years of experience with Python;
- 2+ years of experience as a Data Engineer;
- Experience with Pandas;
- Experience with SQL DB / NoSQL (Redis, Mongo, Elasticsearch) / BigQuery;
- Familiarity with Amazon Web Services;
- Experience with Kafka;
- Knowledge of data algorithms and data structures is a MUST;
- Working with high volume tables 10m+.

What you'll do:

- Create ETL pipelines and data management solutions (API, Integration logic);
- Different data processing algorithms;
- Involvement in creation of forecasting, recommendation, and classification models.

Would be a plus:

- Experience with Spark (pyspark);
- Experience with Airflow;
- Experience in statistics;
- Knowledge of DS and Machine learning algorithms.

We ensure your growth with:

- Great networking opportunities with international clients, challenging tasks;
- Building interesting projects from scratch using new technologies;
- Personal and professional development opportunities;
- Competitive salary fixed in USD;
- Paid vacation and sick leaves;
- Flexible work schedule;
- Friendly working environment with minimal hierarchy;
- Team building activities, corporate events.

Dataforest is looking for a Data Engineer to join an interesting software development project in the field of Cybersecurity. Our client’s platform offers Identity Insights drives real-time, multi-contextual digital forensics to protect new account opening workflows and expose fake accounts already in a customer database.

You'll be a great fit if you have:

- 3+ years of hands on in data engineering;
- 3+ years of commercial experience with Python;
- Advanced experience with SQL DBs (optimisations, monitoring, etc.);
- Advanced experience with PySpark;
- Solid understanding of ETL principles (architecture/ monitoring/ alerting/search and resolve bottlenecks);
- Familiar with AWS infrastructure (boto3, S3 buckets, etc);
- Experience working with large volumes of data;
- Understanding the principles of medallion architecture.

Would be a plus:

- Cassandra/Scylla
- PostgreSQL
- Pandas
- Experience with Kafka and Redis

What you'll do:

- Maintain data processing architecture using Python and PySpark that interacts with ScyllaDB & PostgreSQL.
- Work with Databricks infrastructure to ensure reliability, scalability, and cost efficiency.
- Proactively identify bottlenecks and suggest technical improvements.

We ensure your growth with:

- Working in a fast-growing company;
- Great networking opportunities with international clients, challenging tasks;
- Personal and professional development opportunities;
- Competitive salary fixed in USD;
- Paid vacation and sick leaves;
- Flexible work schedule;
- Friendly working environment with minimal hierarchy;
- Team building activities, corporate events.

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.

We are the creators of a new fintech era!

Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future.

We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies.

Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA.

Join us as a Head of Data Platform. 

Requirements:
Education & Experience:

— MSc in computer science, mathematics, statistics, physics or another quantitative field; PhD is a plus, not a requirement — research-grade methodology sits with the Quantitative Researcher

— 5+ years in data engineering / analytics, incl. lead / management experience; built a trading / financial data platform from zero at a crypto exchange, HFT / electronic-trading firm, market maker, or trading fintech. Bank / enterprise-DWH background alone does not qualify

— Personally carried production ownership of money data — trades, balances, positions, P&L — where a data error is a financial error

Core Technical Skills — data platform:

— Hands-on today: designs and ships pipelines personally — advanced SQL and Python; ClickHouse (or kdb+ / comparable columnar OLAP) in production

— Streaming and batch in one architecture: Kafka or equivalent, event feeds with second-grade freshness alongside hourly aggregates; trading-day logic — metrics counted within a trading day that starts and ends at a fixed morning hour, with an automatic daily reset

— Optimizes data footprint and computational overhead across every stage: selects the tech stack, partitioning logic, and execution flow based on cost-to-performance impact

— Data-correctness engineering: reconciliation, idempotent backfills, late / duplicate event handling, data-quality monitoring and lag alerting

— Vendor and API integration: institutional market-data providers (Kaiko / Amberdata class), exchange APIs, FX feeds; schema design and documentation others can build on

Core Technical Skills — analytics & applied math:

— Applied statistics at a working level: distributions, hypothesis tests and confidence intervals, correlation vs causation, time-series and aggregation pitfalls (session boundaries, survivorship, outliers) — enough to catch a methodological error in an analyst's query or notebook, and to have designed or reviewed metric / experiment methodology in past roles

— Metric and semantic design: one written definition per metric (P&L, uptime, spread, fill rate) enforced across every report; analytical data models (marts / star schemas) that make those definitions computable and cheap to query

— BI stack governance: Tableau / Grafana — performance, access control, versioned metric definitions; reviews dashboards for correctness rather than building them all himself

— ML literacy: understands when a model is — and is not — needed and can supervise applied work; research-grade modeling remains the Quantitative Researcher's mandate

Market & Domain Knowledge:

— Trading data from practice, not textbooks: maker/taker, fees and rebates, perpetual funding, realized / unrealized P&L, inventory and exposure, order-book depth and spread, slippage, quoting uptime

— Understands the consumers: how analysts, quants and desk heads use the data, and how market-quality / compliance metrics feed liquidity-provider terms

— Has built and run a mixed organisation of data engineers and analysts: separate hiring bars and growth tracks, peer review of queries and dashboards as a standard, methodology documented before a number ships

— Partners with quantitative research: turns the Researcher's methodology into production metrics and dashboards without distorting it; runs the sign-off process with quant and desk stakeholders

— Treats balances and client data as confidential by default: access control, least privilege, audit trail, no raw-data leaks

— Never ships silently wrong numbers: escalates early, cuts scope consciously, flags uncertainty in the data

Mindset & Soft Skills:

— Player-coach: the first months are personal hands-on delivery; scaling through the team runs in parallel, not instead

— Speaks business: reports status in money and deadlines; prioritizes by P&L impact — which dataset unblocks which decision; no jargon walls with management and stakeholders

— Owner of the direction's technical goals, not of a backlog: takes a business problem, decomposes it into tasks, executes and delegates

Responsibilities — Platform:

— Architect and ship the central data warehouse: every trade, position, balance and P&L across all trading services; an hourly contour for BI plus a real-time contour for monitoring; trading-day logic with automatic daily reset

— Build the data layers behind LP terms: market quality (spread, depth near mid price, fill rate, per-order slippage), quoting-uptime start/stop events per maker and pair, external tier-1 benchmarks and FX feeds for regional fiat pairs

— Close the data debt: gaps in realized-P&L coverage, history backfills, broken monitoring services — restore or replace

— Set and hold agreed data-freshness targets and pipeline-health alerting; own schemas and documentation

— Own analytics delivery end-to-end: the KPI rulebook published and signed, financial / attribution / historical-performance dashboards, compliance screens and provider scorecards

— Enforce a single semantic layer: one definition per metric across every report; methodology documented before a dashboard ships

— Quality gate: numbers are reviewed and challenged before they reach management; sign-offs from quant / desk collected per deliverable

Responsibilities:
— Hire leaders-first and onboard the technical team (~10 people: data engineering, analytics & BI, technical PM) per the approved hiring plan; hand tasks down without losing velocity

— Single interface to exchange tech (requirements, acceptance, freshness targets) and to analytics / quant stakeholders; run delivery in Jira against the approved one-year roadmap

— After the build: run the platform as a service to the direction — researcher-generated tasks, new metrics and pair groups, regular recalibration. 

Working terms

Immerse yourself in Crypto & Web3:

• Master cutting-edge technologies and become an expert in the most innovative industry.

Work with the Fintech of the Future:

• Develop your skills in digital finance and shape the global market.

Take Your Professionalism to the Next Level:

• Gain unique experience and be part of global transformations.

Drive Innovations:

• Influence the industry and contribute to groundbreaking solutions.

Join a Strong Team:

• Collaborate with top experts worldwide and grow alongside the best.

Work-Life Balance & Well-being:

• Modern equipment.
• Comfortable working conditions, and an inspiring environment to help you thrive.
• 22 business days of paid leave.
• Additional days off for national holidays.

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're looking for a Senior SysOps Engineer to join our friendly and professional team.

The platform is a high-traffic web service handling tens of millions of requests per day, with a data pipeline processing 50M+ user events daily that feeds both customer-facing analytics and billing. It runs on self-managed infrastructure across two datacenters, with a mix of long-lived legacy components and an active modernization program. 
 

Engagement: full-time (ideally) or part-time (20+ hours/week).
Location: UA/EU, remote.
 

Required Skills:

• 5+ years of experience as a SysOps Engineer / Infrastructure Engineer (or similar role).
• Strong expertise with OS: FreeBSD (preferred), Linux (Debian, Ubuntu), Jail, Docker, and ZFS optimization.
• Database experience: MariaDB/MySQL with master-master, master-slave replication, MyISAM, InnoDB, GTID.
• Experience with Redis & Sentinel: setup, replication, and failover strategies.
• Experience with Web-servers: Caddy, Lighttpd, Nginx.
• Knowledge of ElasticSearch high-load clusters (replication, scaling, performance tuning).
• Experience with Cloudflare (DNS management, caching, security configurations).
• Experience with Supervisord, Beanstalkd, RabbitMQ.
• Networking & security expertise: Strong understanding of network protocols, packet filtering (iptables, ipfw, pf).
• Proficiency in scripting & automation: Bash, Python, PHP (or similar).
• Experience with PowerDNS and DNS management strategies.
• English - B2 and higher (for effective communication with stakeholders).

Responsibilities:

• Maintain, manage, and improve the existing infrastructure and platform.
• Troubleshoot and resolve issues with critical platform components (ElasticSearch, Redis, FreeBSD, database replication, etc.).
• Maintain and update incident runbooks for the on-call teams.
• Automate system tasks and reduce manual repetitive work.
• Document and optimize deployment processes.
• Work on high-availability solutions for branded domains, database failover, and scaling improvements.

Will Be a Plus:

• Experience with high-load multi-source database replication, Orchestrator, and ProxySQL.
• Knowledge of high-load infrastructure solutions and best practices.

What we offer:
 

• Official B2B contract with a Canadian Company;
• Compensation for medical insurance (for our employees based in UA);
• Flexible working hours (start at 8.00-11.00 o'clock);
• Weekends according to the Ukrainian/Local calendar;
• Engaging projects with opportunities for career growth;
• Certifications, Udemy courses, Claude;
• Corporate events, team-building activities,
• Gifts and recognition from the company;
• A friendly, open, and supportive team culture without excessive bureaucracy.

We're looking for a DevOps Engineer to join our team and work embedded with one of our key clients — a fast-growing AdTech SaaS company operating at scale on AWS. You'll be part of a small, high-ownership DevOps team working closely with the client's backend, data, and product engineers on a daily basis.

This is a hands-on role. You'll handle everything from routine infrastructure maintenance to complex migrations, security hardening, and cost optimization — and you'll be expected to communicate clearly with both technical and non-technical stakeholders.
 

What you'll work on:

• Managing and evolving cloud infrastructure on AWS (EKS, RDS, ElastiCache, S3, OpenSearch, Cognito, SES, and more)
• Maintaining and improving CI/CD pipelines built on GitHub Actions and ArgoCD
• Writing and maintaining infrastructure-as-code with Terraform (including Terraform Cloud)
• Supporting Kubernetes clusters (EKS) with Karpenter autoscaling and Bottlerocket OS
• Participating in security initiatives — MFA enforcement, WAF configuration, GitHub branch protection, GPG commit signing, ISO 27001 compliance
• Monitoring and alerting via Datadog and responding to incidents
• Managing AWS Bedrock integrations and supporting AI-powered services
• Supporting developer teams with access management, environment provisioning, and day-to-day requests
  Contributing to cost optimization efforts and monthly AWS cost reporting

What we're looking for
 

Required:

• Experience with ArgoCD or other GitOps tools
• Solid understanding of AWS core services (EC2, S3, RDS, IAM, VPC, EKS)
• Hands-on experience with Terraform
• Familiarity with Kubernetes and Helm
• Experience with CI/CD pipelines (GitHub Actions or similar)
• Comfortable working in Linux environments and with CLI tooling
• Strong written communication in English — you'll be interacting with client stakeholders daily
• Ability to work independently, ask the right questions, and take ownership

Nice to have:

• Familiarity with Datadog or similar observability platforms
• Knowledge of AWS security services (WAF, GuardDuty, Security Hub)
• Experience with ElastiCache, OpenSearch, or Databricks
• Exposure to ISO 27001 or SOC 2 compliance processes
• Familiarity with AWS Bedrock or AI/ML infrastructure

What to expect

• You'll be embedded in a real product team, not bouncing between projects every few weeks
• Small team, high ownership — your work matters and is visible
• Direct communication with client engineers and stakeholders
• Fully remote, async-friendly, with regular syncs
• You'll be expected to grow into the role — we invest in people who take initiative

What we offer:
 

• Paid vacation — 16 days per year;
• Documented (16 days) and undocumented (5 days) sick leave;
• Leave for significant life events;
• Compensation for medical insurance (for our employees based in UA);
• Flexible working hours (start at 8.00-11.00 o’clock);
• Weekends according to the Ukrainian/Local calendar;
• Engaging projects with opportunities for career growth;
• Certifications, Udemy courses;
• Corporate events, team-building activities,
• Gifts and recognition from the company;
• A friendly, open, and supportive team culture without excessive bureaucracy.

Technology stack

Tableau, SQL, Snowflake

Client

Our client is one of the world's top 20 investment companies, headquartered in the United Kingdom. The organisation is making significant investments in its data and reporting capabilities, consolidating multiple data sources into a unified ecosystem that supports investment, performance, and client reporting across the business.

Project overview

The role sits within the client's performance and reporting function and supports reporting used across the investment business. The assignment is secured for an initial 6 months with possible extension. Working hours: aligned with London time.

Position overview

We are looking for a Tableau Engineer to join the client's reporting team as part of a team augmentation initiative. This role covers the entire journey from data to dashboard, including data preparation and modelling, as well as the design and development of reports and dashboards. This is a business facing position where you will work directly with internal stakeholders to gather and refine reporting requirements and translate them into clear, reliable, and actionable dashboards.

Responsibilities

• Design, build and maintain dashboards and visualisations in Tableau
• Prepare and model data for reporting, writing and optimising SQL across multiple sources
• Work with internal business users to gather requirements and translate them into reporting solutions
• Ensure accuracy and consistency of data across reports and dashboards
• Improve dashboard performance and usability
• Document report logic and support end users in adopting the solutions

Requirements

• Proven hands-on Tableau development experience, including building dashboards for business users
• Strong SQL skills and solid understanding of data modelling principles
• Experience preparing and structuring data for reporting
• Experience gathering requirements directly from business stakeholders and translating them into reporting solutions
• Strong communication skills and fluent spoken English
• Willingness to work to a London-aligned schedule

Nice to have

• Experience with Snowflake or a similar cloud data warehouse
• Experience in asset management, investment management or wider financial services
• Understanding of core financial metrics and investment terminology

Responsibilities

• Design and improve perception algorithms for autonomous driving applications.
• Develop solutions for 3D object detection, semantic segmentation, object tracking, and drivable area estimation.
• Create and optimize automated data labeling pipelines to improve dataset quality while reducing manual annotation efforts.
• Implement reliable multi-object tracking algorithms operating in 3D environments.
• Train, evaluate, and optimize large-scale deep learning models using extensive datasets and high-performance computing infrastructure.
• Validate developed solutions through testing in real autonomous vehicle platforms.

Requirements

• Master's or PhD degree in Computer Science, Robotics, Artificial Intelligence, or a related technical field (or equivalent industry experience).
• Strong practical experience with deep learning for at least one sensor type, such as cameras, LiDAR, or radar.
• Experience working with modern neural network architectures, including Transformer-based models.
• Solid programming skills in Python and C++.
• Ability to work collaboratively in a fast-paced engineering environment with a strong willingness to learn.

Nice to Have

• Previous experience in autonomous driving, ADAS, robotics, or computer vision.
• Familiarity with deployment and optimization frameworks such as TensorRT, ONNX Runtime, or LibTorch.
• Experience contributing to medium or large-scale software systems.
• Understanding of performance optimization for machine learning inference.

What We Offer

• Opportunity to work on advanced AI technologies shaping the future of mobility.
• Highly technical environment with minimal bureaucracy and fast decision-making.
• Flexible working hours and hybrid work options.
• Competitive compensation package.
• Long-term growth opportunities within an innovative engineering team.
• Equity or long-term incentive program.

Are you passionate about building reliable, scalable, and highly available production systems? Do you enjoy solving complex engineering challenges through automation, observability, and software engineering?
Optimove is looking for a Senior Site Reliability Engineer (SRE) to join our global SRE organization. In this role, you'll partner with engineering teams globally to improve the reliability, scalability, and operational excellence of our cloud platform.
As a Senior SRE, you'll lead engineering-driven reliability initiatives by building automation, improving deployment processes, advancing observability, and driving operational excellence across our production environment.

Responsibilities
 

• Reliability Engineering – Design and implement solutions that improve the reliability, availability, and scalability of our production platform while proactively identifying and eliminating operational risks.
• Automation & Platform Engineering – Build internal tools and automation that eliminate manual operational work, improve engineering productivity, and streamline production workflows.
• Observability – Improve monitoring, alerting, dashboards, and production visibility while driving initiatives that reduce alert fatigue, improve incident detection, and strengthen operational insights.
• Production Rollouts – Design, improve, and support production deployment processes using modern release strategies such as Canary, Blue/Green, and Feature Flags to ensure safe and reliable releases.
• Production Reliability & Incident Response – Lead the investigation and resolution of critical production incidents, perform root cause analysis, and implement long-term preventive improvements that improve platform resilience and reduce recurring issues.
• On-Call & Operational Excellence – Participate in the team's on-call rotation, ensuring reliable production operations. Respond to critical incidents, collaborate with engineering teams during service disruptions, and drive engineering improvements, automation, and operational best practices that reduce operational toil and prevent future incidents.
• Cloud & Infrastructure – Design, maintain, and improve our Kubernetes-based cloud platform, CI/CD pipelines, and production infrastructure running on GCP and AWS.
• Engineering Partnership – Collaborate closely with Software Engineers, DevOps, DBAs, and Product teams to improve reliability across the software development lifecycle and promote SRE best practices.
   

Requirements
 

• 5+ years of experience in Site Reliability Engineering, Platform Engineering, DevOps, or Infrastructure Engineering.
• Hands-on experience operating Kubernetes in production environments.
• Strong experience working with public cloud platforms (GCP or AWS).
• Strong programming and scripting skills (Python preferred; Go or Bash are a plus).
• Experience designing and building automation and internal engineering tools.
• Experience working with CI/CD pipelines and modern deployment methodologies.
• Hands-on experience with observability platforms such as Datadog, Prometheus, or Grafana.
• Strong understanding of Linux, networking, distributed systems, and cloud-native architectures.
• Excellent troubleshooting, debugging, and root cause analysis skills.
• Strong communication skills and experience collaborating with globally distributed engineering teams.
   

Advantages
 

• Experience with Infrastructure as Code (Terraform, Ansible, etc.).
• Experience with messaging and distributed technologies such as Kafka, Pub/Sub, or Redis.
• Experience supporting modern deployment strategies such as Canary, Blue/Green, or Feature Flags.
• Familiarity with OpenTelemetry and modern observability tooling.
• Understanding of Site Reliability Engineering principles, including SLIs, SLOs, and error budgets.
• Experience working in large-scale SaaS production environments.
• Relevant cloud or Kubernetes certifications (GCP, AWS, CKA, CKAD).
  
  Why Join Us?
  Join a team where Site Reliability Engineering is treated as an engineering discipline—not just an operations function. You'll work on large-scale cloud infrastructure serving enterprise customers while building automation, improving observability, and enabling engineering teams to deliver software safely and reliably.
• You'll collaborate with a global SRE organization using modern cloud-native technologies, Kubernetes, distributed systems, and large-scale SaaS workloads. You'll have the opportunity to lead impactful reliability initiatives, influence engineering best practices, and solve complex production challenges that directly improve the experience of both customers and engineering teams.

Meet the YozmaTech

YozmaTech isn’t just another tech company – we’re a global team of go-getters, innovators, and A-players helping startups and product companies scale smarter and faster. We build dedicated development teams across 10+ countries, creating strong, long-term partnerships based on trust, transparency, and real impact. Here, every idea counts. We value people who are proactive, open-minded, and ready to grow. If you’re passionate about building meaningful products and want to join a team that feels like family – you’ll feel right at home with us.

Our client is a CX AI platform that enables enterprises to deliver intelligent, automated, and personalized customer experiences across all digital channels. Powered by advanced AI, large language models, and real-time decisioning, the client transforms customer interactions into proactive, context-aware journeys-boosting efficiency, empowering agents, and driving measurable business outcomes at enterprise scale.
As a Data Engineer, you will play a key role in designing, building, and maintaining the data infrastructure that powers our products and drives business insights. This is an opportunity to join a fast-moving, innovation-driven team where your work will directly impact how we leverage data to serve global enterprises.

Key Requirements:
🔹4+ years of experience as a data engineer or similar role;
🔹Expert-level proficiency in SQL (complex queries, performance tuning, database design);
🔹Strong proficiency in Python for data processing and automation;
🔹Solid understanding of data modeling, data warehousing concepts, and ETL/ELT methodologies;
🔹Strong problem-solving skills, attention to detail, and ability to work independently.
🔹English – Upper-Intermediate or higher.

Will be plus:
🔹Experience with cloud data platforms (AWS Redshift, Azure Synapse, Google BigQuery, or Snowflake);
🔹Familiarity with data orchestration tools (Airflow, dbt, Luigi, or similar);

🔹Experience with Tableau;
🔹Background in working with large-scale datasets and big data technologies.

What you will do:
🔹Design, build, and maintain scalable data pipelines and ETL/ELT processes.
🔹Develop and optimize complex SQL queries, stored procedures, and database structures.
🔹Create and manage data models, data warehouses, and data lakes.
🔹Ensure data quality, integrity, and availability across systems.
🔹Collaborate with analytics, product, and engineering teams to support data-driven decisions.
🔹Monitor and troubleshoot data pipeline performance and reliability.
🔹Document data architecture, processes, and best practices.

Interview stages:
🔹 HR Interview;
🔹 Technical Interview;
​​​​​​​🔹 Technical Interview;
🔹 Reference Check;
🔹 Offer.

Why Join Us?

At YozmaTech, we’re self-starters who grow together. Every day, we tackle real challenges for real products – and have fun doing it. We work globally, think entrepreneurially, and support each other like family. We invest in your growth and care about your voice. With us, you’ll always know what you’re working on and why it matters.

From day one, you’ll get:
🔹 Direct access to clients and meaningful products;
🔹 Flexibility to work remotely or from our offices;
🔹 A-team colleagues and a zero-bureaucracy culture;
🔹 Opportunities to grow, lead, and make your mark.

After you apply

We’ll keep it respectful, clear, and personal from start to offer.
You’ll always know what project you’re joining – and how you can grow with us.

Everyone’s welcome

Diversity makes us better. We create a space where you can thrive as you are.

Ready to build something meaningful?

Let’s talk. Your next big adventure might just start here.