Top Data Engineer Jobs in Poland

Lead the technical architecture and long-term strategy for GIS data pipelines and platforms. Drive cross-team GIS engineering initiatives, select and adopt tools (ArcGIS, FME, cloud GIS), enforce data engineering standards, and ensure scalable, reliable geospatial products. Act as technical reviewer, mentor engineers, collaborate with solution architects and customers, and oversee QA/QC, performance optimization, and multi-format data harmonization.

Design, build, and maintain large-scale data processing systems and pipelines (Spark, BigQuery, dbt/Dataform). Improve scalability, performance, and reliability; own projects end-to-end; extend and operate Airflow; work with cloud (GCP/AWS), Kubernetes, and CI/CD practices.

As a Principal Big Data Engineer, you will design scalable solutions, develop data pipelines, create features, and collaborate with various teams to enhance Akamai's data platform.

Design, build, and optimize Databricks-based data pipelines and platforms to support analytics and data products. Collaborate with data scientists, analysts, and stakeholders to deliver high-quality datasets, implement data quality and governance practices, troubleshoot performance issues, and support deployment, monitoring, and documentation of data solutions.

The Data Engineer will design and maintain data pipelines, optimize performance, enforce governance, and collaborate with AI teams for innovative solutions.

The Senior Data Engineer will design, build, and maintain data systems, pipelines, and infrastructure for data processing and analysis, collaborating with stakeholders and ensuring data quality.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

The Credit Risk Data Engineer drives technical activities, ensures compliance of batch engines, and liaises with analysts to implement functional requirements for credit risk models.

Support integration of AI and automation across the data platform, build ETL and data quality checks using Dataiku, prepare data for reporting and analysis, improve reporting via ML and automation, collaborate with analysts and stakeholders to deliver reliable AI/data solutions and identify data improvement opportunities.

Design and optimize databases and data processing systems, collaborate with developers, gather user requirements, and provide user support.

Design, build, and operate GitOps-driven data and AI platforms and pipelines (ETL/ELT, feature stores, batch/real-time inference). Enable GenAI/RAG (embeddings, vector ingestion), implement IaC and CI/CD, enforce data quality, governance, observability and MLOps, and support Azure/AWS-based production deployments while collaborating with security, product, and operations teams.

Develop and maintain production applications and data pipelines supporting scientific workflows. Build APIs, backend services, and UIs; design ETL, data validation, and storage solutions; collaborate with scientists and senior engineers; participate in code reviews, CI/CD, deployments, and production support to ensure reliable data-driven research infrastructure.

Design, model, and develop AWS-based data ecosystems and pipelines. Gather and document business and technical requirements, optimize data storage (lakes/warehouses), tune performance, troubleshoot processing and infrastructure issues, integrate diverse data sources, mentor junior engineers, and engage directly with clients.

Design, build, and maintain scalable ETL/ELT pipelines and dbt models to ingest, transform, enrich, and ensure quality of firmographic data. Support entity resolution/matching, monitor pipelines with alerts and metrics, collaborate with product and data teams, and document standards for maintainability.

As a Data Engineer, you'll design robust data pipelines, optimize data architectures, ensure data quality, and support analytics efforts while collaborating with stakeholders.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

As a Senior Robotics Data Engineer, you will manage vision system data streams, ensure data quality for AI training, and collaborate on innovative robotics solutions.

Build and maintain Python-based data pipelines on Databricks, collaborate with product and domain experts, debug Tier-2 system issues, create documentation and onboarding processes, write integration tests, manage code in GitLab, and improve data quality and self-service tooling.

The Analytics Engineer will design and implement data integration solutions (ETL) for data analytics, build data models, and improve data infrastructure.

Design, develop, and optimize Informatica PowerCenter mappings, workflows, and PowerExchange integrations across Oracle, DB2, Snowflake, Linux, and mainframe systems. Perform SQL/PLSQL development, database and ETL performance tuning, shell scripting for automation, and support production operations and incident management while translating business requirements into scalable data integration solutions.

Build and maintain ELT pipelines, develop and document dbt models, optimize and query Google BigQuery, write Python for data processing, ensure data quality, monitor and troubleshoot pipelines, and collaborate with analytics, product, and game teams to deliver reliable analytics datasets.

Design, build, and maintain ELT pipelines; develop, test, and document dbt models; optimize and query Google BigQuery; write Python for data processing; ensure data quality and reliability; monitor and troubleshoot pipelines; collaborate with analytics, product, and game teams and influence data architecture.

The AI/NLP/Data Engineer will develop ML and AI models, enhance NLP systems, and build supporting infrastructure while collaborating with teams on deployment and documentation.

Build, maintain, and scale ELT data pipelines and dbt models on Google BigQuery. Write Python for data processing, ensure data quality, monitor pipelines, collaborate with analytics/product/game teams, and contribute to data architecture in a fast-paced startup.