Top Data Engineer Jobs in Warsaw

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design, build, and maintain scalable Databricks ETL/ELT pipelines using Python and SQL. Optimize cloud data workflows, support data models and integrations across Azure/AWS/GCP, monitor and troubleshoot pipelines, and collaborate with analysts and data scientists to deliver clean data for AI and analytics.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Lead design and build of cloud-native, production-grade data platforms and real-time pipelines. Own architecture, implement scalable ingestion, storage, processing, and access layers, ensure security and governance, optimize performance, mentor engineers, and consult directly with clients to deliver data products and solutions.

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.

Build and scale ad‑tech data infrastructure and ETL/ELT pipelines ingesting SSP/DSP/GAM and ancillary data, maintain data warehouses/lakes, implement data quality and privacy controls, and support BI/analytics for real‑time campaign and auction 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.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

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.

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.

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.

Build and optimize production-grade data pipelines that convert AST outputs into knowledge graphs and vector embeddings, integrate Neo4j and Qdrant, orchestrate self-hosted LLMs for automated documentation, ensure data lineage and integrity, implement testing and monitoring, and collaborate with AI/ML engineers to improve RAG retrieval and validation under strict contractual KPIs.

Design and maintain high-throughput data pipelines that integrate AST outputs, knowledge graphs, and vector indexes; build Neo4j graph schemas; integrate Tree-sitter and Qdrant embeddings; ensure data lineage, snapshot versioning, validation, and monitoring; collaborate with AI/ML engineers to optimize retrieval and LLM orchestration for automated code documentation.

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, 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.

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.