Top Data Engineer Jobs in Poland

Design and build scalable real-time and batch data pipelines for programmatic ad data (bid streams, impressions, revenue). Ingest and normalize SSP/DSP/GAM data, implement ETL/ELT workflows, build and maintain data warehouses/lakes, enable auction analytics and yield optimization models, ensure data quality and privacy compliance, and support BI/analytics teams.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Develop software for risk data pipelines, collaborating with trading teams to manage trade data, improve capital efficiency, and automate operations.

The Senior Data Engineer will design and evolve the internal Data Platform, focusing on architecture, data engineering, and mentoring while ensuring standards for data quality and governance.

Develop and maintain a cloud-native data ecosystem focusing on metadata management and data quality, integrating solutions with analytical tools, and collaborating with various teams.

Build and maintain high-throughput data pipelines, design schemas and data models, implement IaC and CI/CD, collaborate with Data Science/AI on feature stores and MLOps, monitor containerized systems, and advise stakeholders on data architecture.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Develop and enhance BI solutions using SAP technologies, ensuring best practices in performance, data integration, and training users on SAP Data & Analytics.

Design and build reusable Databricks Lakehouse PySpark pipelines, implement data models (fact/dimension, SCD Type 2), enable incremental/idempotent loads, optimize performance, implement event-driven integrations, collaborate on Azure cloud resources, and improve data platform architecture.

Design, develop, and maintain data-driven solutions for regulatory and product stewardship applications. Translate business requirements into scalable implementations, write and optimize SQL, troubleshoot data issues, collaborate with cross-functional teams, and support large-scale regulatory data products through planning, testing, and deployment.

Design, build, and operate Azure-based data platform environments and lead migrations to Azure/Databricks/Microsoft Fabric. Implement IaC (Terraform), Kubernetes platform operations, CI/CD automation, security and governance controls, monitoring/observability, and cost/performance optimizations. Collaborate with data engineers, architects, and stakeholders and produce technical documentation while providing technical leadership during the migration.

Design, build, and maintain data platform tooling and developer self-service capabilities. Build CLI tools, Data Product templates, Airflow components, Terraform/IaC assets, GitOps integrations, telemetry, and Agentic AI workflows while collaborating with platform, data engineering, analytics, and ML teams.

Design and implement scalable cloud data infrastructure, ETL pipelines, data models, and warehouses. Develop data processing solutions in Python and SQL, integrate legacy and modern systems, prepare data for AI use cases, gather requirements, produce technical specifications, scope releases, manage code reviews, and liaise with customers.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design, build, and maintain analytics-ready datasets and ETL for cybersecurity reporting; develop scripts and dashboards for tools like CrowdStrike, Prisma/Cortex, and MS InTune; create Power BI on-demand and static reports; translate security objectives into measurable metrics and present to technology leaders.

Lead Data Engineer to own and evolve an AWS-native data platform powering underwriting intelligence and GenAI. Build scalable ingestion, transformation, storage, and serving layers using Python and .NET, Aurora PostgreSQL and OpenSearch. Define data architecture, enforce data quality and observability (CloudWatch, Splunk), support LangGraph-based RAG and LLM workflows, drive architecture and deployment decisions, mentor engineers, and collaborate with product and AI teams to deliver reliable, production-grade data systems.

Lead technical design and delivery of enterprise-scale data platform capabilities (ingestion, ETL/ELT, governance, observability). Influence architecture and standards across squads, mentor engineers, drive scalability, performance, CI/CD, and platform reliability in a cloud (Azure) environment to enable analytics and AI use cases.

Lead and own the data ecosystem: architect, design and maintain scalable data pipelines (Azure Data Factory), build and optimize data models in SQL Server, develop Python/PySpark automation, implement Databricks/Spark workflows, resolve pipeline issues, improve data quality and resilience, and mentor junior engineers while aligning data solutions to business needs.

Lead design and implement CDC-based data architecture (Postgres -> Kafka -> Snowflake). Build streaming/near-real-time pipelines, data quality controls, data contracts, tenant separation, replay/reload mechanisms, and external delivery. Start hands-on and grow a small data engineering team; deliver a pragmatic 3/6/12-month roadmap.

As a Data Engineer, you will lead the development of scalable data pipelines, manage production datasets, and mentor engineers while collaborating with Data Science and Analytics teams.

Lead design and implementation of data solutions and scalable ELT/ETL pipelines to ensure regulatory compliance and legal process automation. Maintain data integrity across large datasets and production systems, modernize architecture, refactor complex flows, optimize alerts, and collaborate with Legal, Compliance, and engineering teams.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Design, build, and maintain large-scale batch and real-time data pipelines for high-volume advertising data using Java/Scala/Python with Spark and Flink. Ensure data quality, optimize performance, improve observability, integrate external sources, and contribute to platform architecture and cross-team design reviews.

The Data Engineer will construct data pipelines, manage ETL processes, optimize SQL queries, and ensure data integrity, utilizing various tools such as Airflow, Python, and SQL.