Top Data Engineer Jobs in Krakow

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.

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 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.

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.

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.

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.

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.

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