Jobs Data Engineer

Project description

As the Platform Integration Planning & Data Enablement Lead, you will support the Platform Systems Integration function by enabling effective planning, governance and decision-making through robust data management, reporting, automation and process improvement. The role is responsible for centralising planning data, developing automated reporting solutions, and creating actionable management outputs that support programme delivery, resource planning and strategic decision-making.

You will act as the central point for planning data and insight — building and maintaining dashboards, automating reporting, and turning complex planning, delivery and resource data into clear, trusted and accessible information for senior stakeholders. This requires a blend of programme planning expertise and hands-on data capability (Tableau, Google BigQuery, Python).

Additionally, you will design and maintain data governance standards (quality, traceability, consistency), support operational governance forums through data-driven insights, and drive continuous improvement and reporting automation across planning and operational processes

Responsibilities

Planning Data & Centralisation: Establish and maintain centralised planning and operational datasets as a single source of truth for Platform Integration.

Automated Reporting & Dashboards: Design, build and maintain automated reporting solutions and dashboards (primarily Tableau), reducing manual effort and improving reporting accuracy.

Data Analytics & Insight: Analyse planning, delivery and resource data to identify trends, risks and opportunities; develop forecasting, capacity and scenario-planning models.

Executive Reporting: Create executive-level visualisations and decision-support materials, including governance packs, metrics and KPI reporting.

Data Governance: Design and implement data governance standards to improve data quality, traceability and consistency.

Automation & Tooling: Utilise Tableau, Google BigQuery and Python-based solutions to enhance reporting and operational efficiency and drive reduction of manual processes.

Programme Planning Support: Support Platform Integration planning activities (integration, testing, release and readiness) through data management and analytics capabilities.

Operational Coordination & Governance: Support operational governance forums (Operations Sync) through data-driven insights and reporting.

Cross-Functional Data Integration: Enable cross-functional data integration and information sharing across Platform Integration activities.

Continuous Improvement: Identify opportunities for process improvement and standardisation; maintain an improvement recommendations and implementation roadmap.

Skills

Must have

Programme Planning Expertise: Experience in delivery planning within complex engineering or integration environments.

Data Management & Analytics: Strong data management capability — centralising datasets, ensuring data quality and governance, and turning raw data into insight.

Advanced Tableau: Hands-on advanced dashboard development and maintenance.

Data management experience, including working with HW/SW release data, compatibility tracking and mainline manifest"

Executive-Level Outputs: Ability to produce presentable, decision-ready outputs and reporting for senior leadership audiences.

Stakeholder Communication: High-quality communication and stakeholder management skills.

Process Improvement through Data: Experience driving process improvement and automation through data-led insights.

Planning Tools: Proficiency in planning tools such as MS Project, Jira, Confluence or Primavera.

Nice to have

GitHub / Version Control: GitHub and version-controlled development experience (desirable) — supporting adoption of modern development practices, version control for reporting/data scripts and documentation.

Integration Knowledge: Understanding of integration, release governance and the engineering delivery lifecycle.

Process & Documentation Governance: Familiarity with process authoring and governance discipline (ISO 9001 / QMS alignment).

Continuous Improvement Mindset: Ability to propose and implement improvements in planning and reporting processes.

Google BigQuery: Experience working with BigQuery (or equivalent cloud data warehouse) for data storage, querying and integration.

Python: Programming for automation, reporting and data analysis.

Other

Languages

English: C1 Advanced

Office in Krakow!

We’re looking for a Data Engineer to build scalable data infrastructure for a next-generation transactional platform designed to handle millions of concurrent users.

You’ll help build a greenfield lakehouse, develop reliable streaming and CDC pipelines, and ensure data quality, integrity, and performance at scale.

What you’ll do:

• Build lakehouse architecture using Iceberg, Delta, or similar technologies.
• Develop streaming and CDC pipelines with Kafka and Debezium.
• Optimize data storage, partitioning, and query performance.
• Implement data quality monitoring, governance, and data protection.
• Support data migration and ensure reliable data processing.

What we’re looking for:

• 3+ years of Data Engineering experience.
• Hands-on experience with AWS S3 or similar object storage and Snowflake, Databricks, or BigQuery.
• Experience with Iceberg, Delta, or Hudi.
• Knowledge of Kafka, CDC pipelines, and streaming data.
• Strong SQL, data modeling, and Python skills.
• Familiarity with Airflow, Dagster, and data governance practices.
• Ability to work independently and take ownership.

Nice to have: Experience with financial or transactional data, regulated environments, or greenfield data platforms.

We are seeking an experienced Data Engineer to develop and support reliable data infrastructure across our systems. In this role, you will create scalable data pipelines, maintain data accuracy and consistency, and connect external data sources. Your main goal will be to ensure smooth data movement and strengthen analytics capabilities throughout the organization. You will also help shape our data architecture, apply engineering best practices, and work closely with cross-functional teams to transform raw data into meaningful, actionable insights.

Requirements

• 3+ years of commercial experience in Data Engineering
• Experience gathering business requirements and communicating with stakeholders
• Strong knowledge of Data Warehouse (DWH) architecture and principles
• Hands-on experience designing data warehouses and building ETL pipelines
• Deep practical experience with Python
• Hands-on experience in one or more of the following areas: data processing using PySpark, Apache Spark, Pandas, or similar frameworks; data scraping; integration of third-party data sources and APIs
• Proficiency in SQL and relational databases such as PostgreSQL and MySQL
• Hands-on experience with column-oriented databases such ClickHouse, Snowflake, Cassandra, BigQuery (many tables with millions of records and above each)
• Solid understanding of software design patterns, algorithms, and data structures
• Intermediate English proficiency

Will be a plus

• Experience with messaging platforms such as RabbitMQ or Kafka
• Familiarity with DataOps practices and data pipeline automation
• Familiarity with CI/CD pipelines and Kubernetes environments
• Background in FinTech or Trading domains

Responsibilities

• Communicate with technical and non-technical stakeholders to gather and clarify business requirements
• Review and analyze data and business logic to ensure consistency, accuracy, and reliability
• Design, implement, and maintain data pipelines for efficient and scalable data processing
• Integrate, maintain, and support data solutions and third-party data sources
• Research and evaluate third-party tools, services, and components for potential use
• Follow engineering best practices, including code reviews, testing, refactoring, and continuous delivery
• Design, optimize, and maintain data storage solutions

What we offer:

• Tax expenses coverage for private entrepreneurs in Ukraine
• Expert support and guidance for Ukrainian private entrepreneurs
• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

We are looking for an experienced Data Engineer to design and maintain robust data infrastructure across our systems. In this role, you will be responsible for building scalable data pipelines, ensuring data integrity, and integrating third-party data sources. Your primary focus will be to enable efficient data flow and support analytical capabilities across the organization. You will also contribute to the development of our data architecture, implement best engineering practices, and collaborate closely with cross-functional teams to turn raw data into actionable insights.

Requirements

• 3+ years of commercial experience in Data Engineering
• Experience gathering business requirements and communicating with stakeholders
• Strong knowledge of Data Warehouse (DWH) architecture and principles
• Hands-on experience designing data warehouses and building ETL pipelines
• Deep practical experience with Python
• Hands-on experience in one or more of the following areas: data processing using PySpark, Apache Spark, Pandas, or similar frameworks; data scraping; integration of third-party data sources and APIs
• Proficiency in SQL and relational databases such as PostgreSQL and MySQL
• Hands-on experience with column-oriented databases such ClickHouse, Snowflake, Cassandra, BigQuery (many tables with millions of records and above each)
• Solid understanding of software design patterns, algorithms, and data structures
• Intermediate English proficiency

Will be a plus

• Experience with messaging platforms such as RabbitMQ or Kafka
• Familiarity with DataOps practices and data pipeline automation
• Familiarity with CI/CD pipelines and Kubernetes environments
• Background in FinTech or Trading domains

Responsibilities

• Communicate with technical and non-technical stakeholders to gather and clarify business requirements
• Review and analyze data and business logic to ensure consistency, accuracy, and reliability
• Design, implement, and maintain data pipelines for efficient and scalable data processing
• Integrate, maintain, and support data solutions and third-party data sources
• Research and evaluate third-party tools, services, and components for potential use
• Follow engineering best practices, including code reviews, testing, refactoring, and continuous delivery
• Design, optimize, and maintain data storage solutions

What we offer:

• Tax expenses coverage for private entrepreneurs in Ukraine
• Expert support and guidance for Ukrainian private entrepreneurs
• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

We are looking for AVEVA PI System Engineers at Middle and Senior levels to join our team and work on industrial data solutions for international customers.

You’ll work with the AVEVA PI System ecosystem, helping customers collect, structure, contextualize, analyze, and get value from operational data.

Depending on your experience, you can contribute as a PI System Engineer or take ownership of technical delivery as a Senior Engineer.
 

What you’ll do

• Implement, configure, maintain, and support AVEVA PI System solutions.
• Work with industrial operational data and help structure it into meaningful, reliable information.
• Design and develop Asset Framework (AF) structures and information models.
• Work with Event Frames, PI Analytics, calculations, and data contextualization.
• Troubleshoot technical issues and support PI System environments in production.
• Contribute to PI System implementations, upgrades, migrations, and optimization projects.
• Create technical documentation, reusable templates, standards, and best practices.
• Collaborate with engineers, consultants, and customers to deliver reliable and scalable solutions.
   

For PI System Engineer

We’d expect you to have:

• 2+ years of hands-on experience with AVEVA PI System in application support, implementation, or technical project delivery.
• Good understanding of PI System concepts, including Asset Framework, information modelling, calculations, data contextualization, and operational data integration.
• Experience troubleshooting and supporting PI System environments.
• Ability to work independently while knowing when to involve more experienced colleagues.
• Interest in developing towards a customer-facing PI System consulting role.
   

For Senior PI System Engineer

In addition to the above, you should have:

• 5+ years of hands-on experience implementing, configuring, optimizing, and supporting AVEVA PI System solutions in production environments.
• Strong expertise in Asset Framework, information modelling, Event Frames, PI Analytics, and scalable/reusable PI System data models.
• Experience leading complex implementations, upgrades, migrations, or performance optimization initiatives.
• Ability to independently troubleshoot complex technical issues in business-critical PI System environments.
• Experience acting as a Technical Lead or taking technical ownership of PI System projects.
• Ability to define and promote technical standards, implementation approaches, and best practices.
   

What we offer

• International projects in the industrial / operational technology domain.
• Opportunity to work with experienced PI System professionals and develop your expertise.
• A chance to grow towards a consulting or technical leadership role.
• Remote-first work environment.
• Challenging projects where industrial data and technology have a real business impact.
   

If you have strong AVEVA PI System experience and are looking for your next technical challenge, we’d be happy to hear from you.

We are looking for a Data Operations Manager to take ownership of the reliability, performance, and operational efficiency of our data platform.

We are also open to a strong Data Architect or Senior Data Engineer who has hands-on experience with data platform operations and is ready to take broader ownership of the data environment.

About the Role

You will be responsible for keeping our data platform reliable, scalable, observable, and cost-efficient. This is a hands-on role combining data engineering, platform operations, performance optimisation, data quality, and technical ownership.

You will work closely with Data Engineering, Analytics, BI, Security, and Finance teams to ensure that data is available, accurate, fresh, and delivered within agreed SLAs.

What You’ll Do

• Own and optimise our Snowflake data warehouse, including query performance, warehouse sizing, resource utilisation, and cost efficiency.
• Ensure the reliability and performance of dbt projects, covering data models, incremental processing, automated testing, documentation, and deployment.
• Oversee production ELT pipelines, ensuring data availability, freshness, reliability, and compliance with established SLAs.
• Build and improve monitoring, alerting, and incident management processes for the data platform.
• Lead root cause analysis and implement preventive measures to reduce recurring incidents.
• Establish and maintain data quality standards, automated validation, and observability practices across the platform.
• Manage data platform access controls, governance policies, and compliance requirements.
• Monitor Snowflake usage and credit consumption, identify optimisation opportunities, and contribute to platform budget planning.
• Develop operational runbooks, CI/CD workflows, and engineering best practices for data teams.
• Identify operational bottlenecks and continuously improve the reliability and efficiency of the data platform.
• Collaborate with Data Engineering, Analytics, BI, Security, and Finance teams on platform reliability, data quality, governance, and cost optimisation.

What We’re Looking For

• 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 experience with dbt Core or dbt Cloud, including incremental models, macros, testing, documentation, and deployment.
• Proven experience operating production ELT pipelines using Fivetran, Airbyte, Stitch, or similar tools.
• Strong understanding of DataOps practices, including CI/CD, Git/version control, automated testing, deployment pipelines, and production operations.
• Experience implementing data quality frameworks, monitoring, alerting, observability, and incident response.
• Good understanding of data governance, access management, security, and data platform reliability.
• Ability to troubleshoot complex data platform issues and drive them through to resolution.
• Strong ownership mindset and the ability to work independently across technical and operational areas.

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 with cloud data platform cost optimisation, including Snowflake credit consumption and resource allocation.
• Knowledge of GDPR, SOC 2, or similar regulatory and compliance frameworks.
• Experience defining or improving data platform architecture and engineering standards.
• Previous experience acting as a Data Architect, Technical Lead, or Senior Data Engineer with ownership beyond individual pipelines or projects.

What Success Looks Like

In this role, success means having a data platform that is reliable, observable, predictable, and cost-efficient: pipelines run within SLAs, data quality issues are detected early, incidents are resolved systematically, Snowflake resources are used efficiently, and the engineering team has clear processes and tooling for operating the platform.

Level: Senior Engagement: Long-term contract Location: Europe (EU / EEA / UK), remote Working hours:EU business hours, shifted 2–3 hours later to ensure daily overlap with US West Coast (PST/PDT) mornings

ABOUT THE CLIENT

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

ABOUT THE ROLE

We are looking for a Senior Software Engineer to design, build, and own significant components of the core query engine of a large-scale distributed data platform. You will lead work across query planning, optimization, and execution — driving architectural decisions, mentoring middle engineers, and pushing the platform's performance and scalability boundaries across all major clouds.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

RESPONSIBILITIES

• Architect, design, and implement significant components of the query engine — planner, optimizer, execution operators, memory management, and data access layers.
• Write and review high-quality, performance-critical code in Java and/or C++.
• Own end-to-end delivery of features from technical design through production rollout.
• Drive performance optimization work — profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths.
• Integrate deeply with columnar formats, open table formats, and connectivity drivers.
• Mentor middle engineers, lead code reviews, and set technical standards for the team.
• Contribute to CI/CD architecture and quality processes across Jenkins, containerization, and multi-cloud deployment (GCP, AWS, Azure via Kubernetes / Docker).
• Debug complex, cross-layer issues spanning query planning, distributed execution, memory management, and I/O.
• Partner with US-based engineering leads on design reviews, architectural decisions, and roadmap execution.

REQUIRED QUALIFICATIONS

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Java or C++, with deep understanding of OOP, systems design, memory management, and concurrency. Candidates strong in both are especially valued.
• SQL & Data: Advanced SQL skills and strong understanding of query execution internals — planning, optimization, and operator implementation.
• Data processing systems experience: Demonstrated experience building or extending data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms.
• Engineering experience: 5+ years in backend/systems software engineering, with a track record of owning significant components.
• CI/CD & DevOps: Solid experience with Jenkins and modern engineering workflows.
• Containers & Orchestration: Strong working knowledge of Docker and Kubernetes.
• Cloud: Hands-on experience with at least one major cloud (GCP, AWS, or Azure); exposure to more than one is a strong plus.
• Version Control: Confident with Git / GitHub workflows and rigorous code review practices.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.
• Leadership: Prior experience mentoring engineers, leading technical designs, or owning components end-to-end.

DESIRED SKILLS

• Deep experience with Apache Arrow (columnar in-memory format).
• Hands-on experience with Apache Calcite (SQL parsing, planning, optimization framework).
• Experience with Gandiva or LLVM-based expression compilation and code generation.
• Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi.
• Deep familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Deep understanding of query planning, optimization (cost-based, rule-based), and execution internals.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Track record of performance optimization at the systems level — profiling, vectorization, cache/memory-conscious design.
• Prior experience contributing to open-source data infrastructure projects.
• Please note: We can consider only candidates currently residing in the EU, EEA, or the UK

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help investigate and fix production issues reported by real enterprise customers.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers
• Write performance-conscious code in Java and/or C++
• Investigate and fix production and customer-reported issues under the guidance of senior engineers, including fixes delivered across multiple supported release lines
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data
• Integrate with columnar formats, open table formats, and connectivity drivers
• Contribute to CI/CD and automated testing in Jenkins
• Deploy and validate changes on Kubernetes (GKE/EKS/AKS) across GCP, AWS, or Azure with Docker
• Debug issues across query planning, distributed execution, memory management, and I/O; collaborate with US-based teams on design and code reviews

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 3+ years in backend / systems software engineering
• Strong proficiency in Java or C++ with solid OOP and software design fundamentals, including concurrency and asynchronous programming (comfort with both languages is especially valued)
• Strong SQL and understanding of relational and analytical data systems, including query execution concepts
• Hands-on experience with data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Experience with Jenkins pipelines and modern development workflows
• Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency)
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); exposure to distributed data platforms such as Spark, Snowflake, or Databricks
• Messaging systems: Kafka, NATS, or cloud pub/sub services
• Query planning, optimization, and execution internals
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS), multi-cloud exposure; Terraform
• Performance profiling of latency-sensitive systems

Details

• Engagement: Long-term contract
• Location: Ukraine, remote

The Defense Procurement Agency (DOT) is a national agency responsible for procuring ammunition, UAVs, military equipment, fuel, food, clothing, and other critical supplies for the Armed Forces of Ukraine and the Defense Forces.

Our mission: Strengthening Ukraine by ensuring reliable support for the Defense Forces while fostering the development of the national defense industry.

Our vision: To become the driving force behind the transformation of Ukraine's defense procurement ecosystem—not only operating the system, but continuously improving it.

We are looking for a Head of Data Governance to establish and lead the organization's enterprise Data Governance function. In this role, you will define the vision, policies, standards, and governance framework for managing data across the organization, ensuring high-quality, trusted, and well-governed data that enables informed decision-making and supports large-scale digital transformation initiatives.

Responsibilities

• Lead the design, implementation, and continuous improvement of the organization's Data Governance framework.
• Develop and maintain enterprise-wide data policies, standards, methodologies, and governance processes.
  Establish and enhance Master Data Management (MDM), Reference Data, Metadata Management, and Data Quality practices.
• Define data ownership, stewardship, governance roles, and accountability across business units.
• Develop data quality standards, monitoring processes, and governance controls.
• Collaborate with business stakeholders, analysts, architects, and engineering teams to define enterprise data requirements.
• Contribute to enterprise data models and integration requirements across corporate information systems.
• Prepare governance documentation, methodologies, standards, and best practices.
• Ensure Data Governance principles are embedded into the implementation of new enterprise systems and digital services.
• Build, lead, mentor, and develop the Data Governance team while driving collaboration across the organization.

Requirements

• Bachelor's degree in Information Technology, Applied Mathematics, Economics, Finance, or a related field.
• Proven experience in Data Governance, Data Management, Business Intelligence, Business Analysis, or related disciplines.
• Hands-on experience implementing or improving Data Governance, Master Data Management (MDM), Data Quality, or Metadata Management initiatives.
• Strong understanding of Data Governance principles, data lifecycle management, and enterprise data architecture.
• Experience developing enterprise policies, standards, governance frameworks, or methodological documentation.
• Experience collaborating with business and technology teams to define and implement enterprise data requirements.
• Understanding of enterprise platforms such as ERP, CRM, BI, Data Warehouse, or similar systems.
• Experience leading teams or coordinating cross-functional initiatives.
• Strong analytical thinking, structured problem-solving, and stakeholder management skills.
• English proficiency at Intermediate level or higher.

Nice to Have

• Experience with SQL, Power BI, or other Business Intelligence platforms.
• Experience implementing Business Glossary, Data Catalog, or Metadata Management solutions.
• Knowledge of DAMA-DMBOK, DCAM, or other Data Governance frameworks.
• Experience supporting enterprise digital transformation programs.

What We Offer

• Meaningful work that directly contributes to strengthening Ukraine's defense capabilities.
• Official employment with competitive salary and full social benefits.
• Professional development, learning opportunities, and career growth.
• Structured onboarding and dedicated mentorship.
• A collaborative, supportive, and mission-driven team.
• Modern office near the metro with reliable internet and equipped shelter.
• Opportunity to establish and scale an enterprise-wide Data Governance function within one of Ukraine's most strategically important organizations.

By joining our team, you will contribute to one of Ukraine's largest digital transformation initiatives in the defense sector and help build the future of defense procurement.

Ready to make a real impact? We'd love to hear from you. Apply today!

* By submitting your CV, you consent to the processing of your personal data in accordance with applicable legislation.

** Due to the high volume of applications, only candidates whose qualifications best match the position will be contacted.

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Senior Software Engineer to work on the core query engine of a large-scale distributed data platform. You will own significant components across query planning, optimization, and execution — both driving improvements and acting as a trusted owner of production quality: diagnosing complex customer-reported issues and delivering safe, well-tested fixes across supported release lines. Engineers with staff-level scope and ambitions are very welcome.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Architect, design, and implement query engine components — planner, optimizer, execution operators, memory management, and data access layers
• Diagnose and resolve complex production and customer-reported issues across the full query path — correctness, performance, memory, concurrency, resiliency, and compatibility
• Develop, review, and backport fixes across multiple supported release lines; strengthen regression prevention
• Write and review high-quality, performance-critical code in Java and/or C++
• Analyze query profiles, execution plans, logs, and telemetry to drive performance optimization — profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths
• Integrate with columnar formats, open table formats, and connectivity drivers
• Improve regression tests, diagnostics, and observability; contribute to CI/CD architecture and quality processes in Jenkins
• Mentor middle engineers, lead code reviews, and help set technical standards
• Debug complex cross-layer issues spanning query planning, distributed execution, memory management, and I/O, partnering with US-based engineering leads

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 5+ years in backend / systems software engineering with a track record of owning significant components end-to-end
• Strong proficiency in Java or C++ (comfort with both is especially valued), with deep understanding of OOP, systems design, memory management, concurrency, and asynchronous programming
• Advanced SQL and solid understanding of query execution internals — planning, optimization, operator implementation
• Experience building or extending data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Ability to assess change risk in a mature production system; experience supporting software across multiple versions, branches, or customer environments
• Hands-on experience with Jenkins and modern CI/CD workflows; Docker and Kubernetes
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows and rigorous code-review habits
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• Prior experience mentoring engineers, leading technical designs, or owning components end-to-end
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation / code generation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); background with distributed data platforms such as Spark, Snowflake, or Databricks
• Deep understanding of query optimization (cost-based, rule-based), vectorized execution, memory management, spilling, and caching
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS) and multi-cloud exposure; Terraform; cloud object storage (AWS S3, Azure Data Lake Storage, Google Cloud Storage)
• SaaS operations experience: incident response, profiling, observability, release and backport processes
• Systems-level performance work and open-source contributions to data infrastructure projects

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We are looking for a senior technical consultant with hands-on experience in mobile traffic infrastructure, backend routing, attribution, Keitaro, GEO segmentation, and anti-abuse systems.

Our current architecture is:

iOS App → AWS ECS / ALB → API → Keitaro

We need an expert to review the existing setup and help us design a reliable production-ready traffic routing and filtering architecture.

The main goal is to understand which traffic signals are realistically available within this stack, which of them are worth using, and how to structure the decision logic between the mobile app, backend, AWS infrastructure, and Keitaro.

Areas we would like to review include:

— IPv4 / IPv6
— GEO location and country-level routing
— ISP / ASN data
— VPN / proxy detection
— User-Agent
— Referrer data
— DNS-related signals
— suspicious or known-abuse IP databases
— request-rate limits and anti-bot / anti-abuse rules
— traffic attribution between different apps, sources, GEOs, and offers
— Keitaro tracking parameters, click IDs, subIDs, and postbacks
— offer routing, weighted distribution, caps, and fallback logic

We would also like an assessment of more advanced identification signals, including:

— device fingerprinting
— device-level signals available on iOS
— battery-related or environment signals
— third-party detection APIs / SDKs

We want to understand which of these signals are technically available on iOS, which require changes in the mobile application, what privacy or reliability limitations they have, and whether the additional complexity is justified.

Expected deliverables

We are initially looking for a technical consultation and architecture review, not immediate full-scale development.

We expect the specialist to provide:

— review of our current architecture;
— recommended traffic routing and filtering logic;
— recommended data sources, APIs, or services;
— assessment of accuracy and false-positive risks;
— expected infrastructure and third-party API costs;
— recommendations for Keitaro integration and attribution;
— QA / staging approach for testing the complete flow;
— clear implementation plan for our existing AWS + Keitaro infrastructure.

Preferred experience

Strong practical experience with several of the following is important:

— iOS mobile traffic infrastructure
— backend traffic routing
— AWS ECS / ALB
— Keitaro or similar tracking platforms
— mobile attribution
— server-to-server postbacks
— GEO / ASN / VPN / proxy detection
— anti-bot and anti-abuse systems
— affiliate traffic infrastructure
— high-volume traffic systems

Experience with real production mobile affiliate projects is highly preferred.

Please include a short description of similar systems you have worked with and explain what parts of this stack you personally designed or implemented.

We are open to starting with a paid 1–2 hour technical consultation.

Project Description:

Join our Development Center in Bucharest, and become a member of our open-minded, progressive and professional team. In this role you will be working on projects for one our world famous clients.
You will have a chance to grow your technical and soft skills, and build a thorough expertise of the industry of our client.
On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.

Responsibilities:

We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud. This role focuses on building reliable, cloud native data products that enable advanced reporting, analytics, and decision making across the organization.

Key Responsabilities:

• Build scalable data pipelines: Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting.
• Develop SQL and BigQuery solutions: Write and optimize advanced SQL transformations and build performant, cost‑efficient BigQuery data models.
• Develop Python workflows: Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high‑quality code.
• Design data models and ensure quality: Build robust data models and apply validation practices to maintain accuracy and reliability.
• Build cloud‑native data solutions: Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms.
• Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance.
• Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards.

Mandatory Skills Description:

• 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems.
• Strong expertise in SQL and hands‑on experience building performant datasets in BigQuery (or similar cloud data warehouses).
• Proven experience with Python and PySpark for scalable data processing in distributed environments.
• Solid understanding of data modeling, ELT/ETL patterns, and data quality best practices.
• Experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services.
• Hands‑on experience building scalable data pipelines (batch and near real‑time) in a cloud‑native environment.
• Proficiency with version control, CI/CD pipelines, and automated testing frameworks.
• Ability to troubleshoot and optimize performance across compute, storage, and processing layers.

Nice-to-Have Skills Description:

• Experience with Infrastructure as Code (Terraform, Ansible, Chef).
• Knowledge of shell scripting.
• Experience in financial services or regulated environments

Languages:

English: B2 Upper Intermediate

🌊 Data Engineer: Build the Lakehouse Behind Millions of Players

Adaptiq × a major online gaming operator

The challenge:

Our client, a major online gaming operator, is building a next-generation account management platform as a greenfield project. It runs in a highly regulated, high-volume environment where availability, sub-second performance, data integrity, and full auditability are non-negotiable, including during extreme traffic spikes.

Every transaction becomes data, and that data has to be right. How do you ingest it without losing or duplicating a single event? How do you keep it fresh and reconciled with source systems? How do you store years of history so it’s cheap to keep, fast to query, and GDPR-compliant? These are the questions you’ll answer.

Your role:

We’re looking for a hands-on Data Engineer to build scalable, reliable data infrastructure and production-grade pipelines, with a strong focus on data quality, integrity, and performance.

You’ll own the design, implementation, and operation of the data lake and lakehouse architecture, including key technical decisions. You’ll define how data is stored, partitioned, processed, and monitored, work closely with Data Engineering, DevOps, Data Science, and other stakeholders, and help shape the platform’s long-term scalability and the data migration.

What you’ll do:

• Build and maintain a multi-layer lakehouse (bronze / silver / gold) on Iceberg or Delta, including schema evolution
• Design CDC and streaming ingestion with Kafka, Debezium, or similar, handling late and duplicate events
• Optimize data layout in cloud object storage: partitioning, compaction, file sizing, query performance
• Define retention, archival, immutability, and GDPR-style deletion processes
• Ensure data freshness, quality, and consistency; detect drift and reconcile with source systems
• Implement governance: metadata, lineage, access controls, PII protection, encryption, audit trails
• Monitor pipelines and drive resolution of data anomalies

• Collaborate with Analytics, Finance, and Compliance on requirements, migration, and architecture

   

What you bring:

• 5+ years in data engineering with large-scale data lakes or lakehouse environments
• Production experience with cloud object storage (AWS S3 or equivalent) and an analytics platform such as Snowflake, Databricks, or BigQuery
• Hands-on expertise with Iceberg, Delta, or Hudi, plus solid knowledge of partitioning, file layout, and query optimization
• Experience building CDC and streaming pipelines (Kafka, Debezium, or similar)
• Strong SQL, data modeling, and understanding of relational and transactional data
• Experience with data quality, monitoring, and governance: lineage, access control, PII handling, retention
• Python and workflow orchestration (Airflow or Dagster)

• Ability to own technical decisions and clearly explain trade-offs

   

Bonus points:

✦ Financial, transactional, or ledger data
✦ Regulated or audit-heavy environments: fintech, gaming, banking, payments
✦ Large-scale data migration projects

If you want your pipelines to carry real money, real scale, and real responsibility, let’s talk.

We are looking for a Data  Engineer to own the reliability and performance of our data platform. You’ll collaborate with engineering and business teams to ensure a reliable, scalable data environment.

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.

Kraków, Poland. Hybrid — 2 days per week from the office.
Own Caliente's data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

Kraków, Poland. Hybrid — 2 days per week from the office. 

What you'll be doing

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.
• Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.
• Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.
• Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.
• Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.
• Monitor ingestion health, data anomalies, and cloud storage/compute spend.

Must-have

• Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.
• Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.
• CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.
• Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.
• Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.
• Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.
• Cloud object storage — S3 or GCS, plus storage tiering and archival.
• Python and an orchestrator — Airflow or Dagster, as tools.

Nice to have

• Fintech, iGaming, or another regulated, audit-heavy environment.
• Cost monitoring / FinOps for storage and compute spend.
• Hudi specifically; Dagster specifically.
• Immutability / WORM regulatory retention.