Top Data Engineer Jobs in Poland

The role involves managing and transforming high volume data to solve business issues related to fraud and compliance, using big data technologies while working in Agile sprints and collaborating with various stakeholders.

As a Lead Python Engineer, you will build a multitenant data platform and data ingestion pipelines on AWS, collaborating with various teams and improving processes.

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

Design, build, and operate scalable data pipelines and integrations (full and incremental) using Scala and GCP Dataflow. Ensure high data quality, develop a unified internal data model, use AI-assisted tools for monitoring, and collaborate with engineering and product teams to optimize production readiness.

Design, build, and maintain scalable data pipelines and integrations (full and incremental) using Scala and GCP Dataflow. Ensure data quality, develop unified internal data models, monitor production pipelines, and collaborate with engineering and product teams while leveraging AI-assisted tools for operations.

Design, deploy, and maintain ML batch pipelines and orchestration (Airflow); support model lifecycle (MLflow); migrate and refactor pipelines to GCP; implement CI/CD, containerization, monitoring, and on‑call support to ensure scalable, reliable, reproducible ML workloads.

Design and implement scalable backend solutions to collect and manage large volumes of data, integrate Veeam products into BaaS offerings, develop and enhance product features, own major components, contribute to architecture, collaborate with product management to define requirements, and estimate implementation effort and capacity.

Design and build scalable, multi-tenant data infrastructure and ETL pipelines, implement real-time ingestion and OLAP optimizations, enforce RLS/RBAC and session-based access controls, ensure data quality and compliance, and integrate analytical databases with embedded BI for enterprise-scale analytics.

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

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.

The Lead Data Engineer will develop enterprise-level data pipelines, oversee data lifecycle management, and provide technical leadership while ensuring data security and compliance.

As a Senior Data Solutions Engineer, you will design scalable data solutions, optimize data pipelines, and collaborate with stakeholders to propose comprehensive systems that meet business needs.

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.

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.

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

Responsible for designing database schemas, optimizing SQL queries, managing cloud data performance, and leading backend development in a microservices environment.

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.

Lead a time‑bounded migration of stateful data systems (Druid, Spark, Redpanda, Airflow, Postgres, Elasticsearch) from bare‑metal VMs to Kubernetes on OpenStack. Codify infrastructure as code (Terraform, Helm/Kustomize, GitOps), operate and tune production data clusters, build observability and SLOs, and own runbooks, KPIs, and incident response for the data platform.

The Senior Data Engineer will design and maintain data pipeline architecture, build ETL infrastructure, and ensure high-quality data for analytics supporting business decisions.

Design, develop and maintain data pipelines and systems to support patient safety and post-market surveillance for medical devices. Build Azure/Databricks-based solutions, automate reporting (Power BI/Microsoft Fabric), support business units, collaborate on data platform initiatives, troubleshoot issues, and support EMEA/global stakeholders.

Design and build analytics-ready datasets, enable self-service semantic layers, translate business questions into metrics, ensure data accuracy and freshness, implement testing and CI/CD for reporting and dashboards across Danaher operating companies.

As a Python Software Engineer, you'll develop Python frameworks, optimize data processing pipelines, and participate in engineering discussions for NAVBLUE's aviation data analytics solutions.

The Staff Data Engineer will manage product analytics architecture, drive data modeling standards, design data systems, and provide technical leadership across teams to improve data insights and product capabilities.