Top Data Engineer Jobs in Poland

Leads the design and development of enterprise data warehouses, lakes, and governed data platforms. Builds scalable batch and real-time ETL/ELT pipelines using Databricks, SQL, Python, and PySpark; drives legacy migration and enterprise data integration. Enables analytics, automation, and AI use cases while establishing engineering standards, mentoring technical teams, and partnering with stakeholders on strategic data transformation initiatives.

Design and build scalable cloud-native data platforms, migrate legacy systems, and develop near-real-time ingestion pipelines. Establish lakehouse, data contract, quality, observability, testing, and CI/CD standards. Optimize Spark and PySpark workloads, create self-service tools and microservices, and support AI-ready infrastructure, agentic workflows, LLM pipelines, and collaboration with machine learning, data science, and product teams.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Design and optimize ETL/ELT pipelines. Collaborate to develop reporting tools. Build and maintain data lakes for ML solutions.

Lead Netflix’s data health analytics strategy by defining data excellence metrics, building trusted data models and dashboards, analyzing lineage and asset utilization trends, and influencing engineering investments. Partner with platform teams to capture metadata and telemetry, standardize data quality evaluations, and measure improvements in developer productivity, data friction, and tooling adoption.

Build and optimize scalable consumer data pipelines, models, schemas, and cloud data lake solutions using Snowflake, dbt, and Python. Ensure data quality, security, privacy compliance, and performance while enabling analytics, reporting, segmentation, and personalization. Collaborate with architects, product owners, and global teams to integrate diverse data sources and implement batch and streaming solutions.

Design, build, and own batch and real-time data pipelines and datasets to support analytics, experimentation, and product insights. Model and organize data for scale and fast retrieval, source from APIs and event streams, translate business requirements into engineering workstreams, collaborate with data scientists and product teams, and mentor colleagues.

Build and maintain scalable data pipelines and software to ingest, process, aggregate, visualize, and analyze system insights and productivity metrics. Collaborate with cross-functional teams to deliver distributed processing solutions, reporting, and experimentation infrastructure, prioritizing data quality and engineering excellence.

Design, build, and maintain full-stack data and AI solutions: scalable data pipelines, GenAI features (RAG, embeddings, LLM assistants), backend APIs/microservices, and frontend dashboards. Deploy and monitor components on cloud platforms, apply engineering best practices, and collaborate with stakeholders to deliver production-ready automation and analytics.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

The Senior Data Engineer will design and build data pipelines on GCP, ensuring high data quality and reliability for analytics and ML products. Responsibilities include mastering ETL processes, managing workflows with Airflow, and collaborating closely with ML engineers.

Join the Customer Data Platform team to deliver production services via Claude Code, own event pipelines, Elasticsearch query/search, sandboxed customer logic, Kafka-backed systems, PostgreSQL-backed services, Kubernetes/Helm/GitOps deployments, coordinate cross-team integrations, scope epics, run on-call rotations, and drive platform custodianship including dependency and CVE migrations.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

The Senior Data Engineer will own end-to-end data pipeline work, maintain production pipelines, integrate new data sources, and collaborate with data scientists.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, build, and deploy Power BI solutions and data models; optimize SQL and DAX performance; manage ETL and real-time ingestion; ensure data quality and warehousing best practices; mentor teammates and contribute to large distributed system design and delivery.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern lakehouse/AI platform. Improve data modelling, quality, reconciliation, performance and operational reliability, partner with stakeholders, and provide technical leadership for delivery.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse/AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Work with stakeholders to deliver production-ready, well-tested, high-performance data products and support operational monitoring and reconciliation.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and maintain backend services, REST APIs, databases, and near-real-time and batch data pipelines. Develop a query engine and scalable insights pipelines, collaborate with product and engineering, improve legacy code, and ensure production monitoring, alerting, and tooling for support.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

Design, build, and own scalable data products on Snowflake/AWS using dbt and Data Vault. Implement ETL/ELT pipelines, CI/CD releases, data quality/observability (Monte Carlo), and governance (Collibra, Immuta). Partner with product and architecture teams, support release automation, and establish engineering best practices within a Data Mesh framework.

Design and implement data models, build ETL workflows and Data Marts, and develop automated reporting dashboards for credit risk. Collaborate with data architects, prepare documentation, and translate business requirements into scalable reporting solutions.

Design and build a scalable cloud data platform on Google Cloud, create reliable batch and streaming ETL/ELT pipelines, enforce data quality and observability, automate IaC/CI-CD, and support analytics, ML, and product teams with high-quality data.