Top Data Engineer Jobs in Warsaw

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design, build, and maintain scalable ETL/ELT pipelines that ingest and normalize ad-tech and ancillary data. Build and optimize data warehouses/lakes, implement data quality monitoring, support BI/analytics teams, and ensure GDPR/CCPA compliance for ad-tech analytics at scale.

Build and scale data pipelines and warehouses for an AdTech BI platform. Ingest and normalize SSP/DSP/GAM log-level and API data, transform raw events into analytics-ready datasets, implement data quality monitoring, support BI teams, and ensure privacy compliance for large-scale, real-time ad analytics.

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.

Maintain and scale production data ingestion pipelines processing billions of events, support and optimize complex ETL and streaming workflows, develop platform tools and ML/streaming initiatives, ensure reliability/observability, and assist data scientists and analysts with production workloads.

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.

Design and build scalable real-time and batch data pipelines for programmatic advertising, ingesting and normalizing SSP/DSP/GAM data, maintaining warehouses, implementing ETL/ELT, ensuring data quality and privacy compliance, and supporting analytics and BI teams.

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, 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, 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 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.

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.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.