Top Data Engineer Jobs in Warsaw

Design and build large-scale analytics pipelines and data models (Spark/Databricks). Define data integrations, lineage, and quality frameworks. Collaborate with product, engineering, and data science to shape long-term data platform architecture, create dashboards and visualizations, and optimize pipelines. Occasional on-call for operational issues.

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.

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.

Build, maintain, and optimize data pipelines, ETL processes, and data models to ensure reliable, scalable data availability. Collaborate with data scientists, analysts, and cross-functional teams to address data quality, governance, and performance; document systems and troubleshoot issues.

As a Data Engineer, you will develop and support big data processing pipelines, build cloud-based infrastructure, and provide visualization solutions for data assets.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Support group HR data alignment, consistency, and reporting across systems (e.g., SAP SuccessFactors). Analyze large datasets, lead data migration tasks, design data processes, monitoring and governance, document mappings and playbooks, ensure GDPR compliance and handle sensitive HR data, and communicate findings to technical and non-technical stakeholders.

The Senior Data Engineer will design and maintain data systems, build data pipelines, optimize ETL processes, and ensure data quality, collaborating with various teams to facilitate data-driven decision making.

The Senior Software Engineer will lead engineering teams, mentor junior engineers, and ensure system reliability and performance while developing backend services.

As a Leading Data Engineer, you will manage a team delivering customer data solutions using Snowflake, ensure platform reliability and compliance, and drive engineering best practices.

Design, develop, and optimize scalable data pipelines on Snowflake. Collaborate with cross-functional teams, implement data modelling and performance best practices, ensure data quality/security, automate workflows, integrate diverse data sources, and monitor/troubleshoot pipelines.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

The Data Engineer will develop, maintain, and optimize data pipelines using Apache Airflow, manage database performance, and support Elasticsearch integration. Responsibilities include building ETL scripts, handling Unix/Linux operations, and implementing CI/CD pipelines in GitLab.

Lead technical data strategy and architecture for Navify Cervical Screening. Drive data integration, governance, and large-scale pipeline design; mentor engineers; evaluate new technologies; ensure compliance, quality, and timely delivery of data requirements across cross-functional teams.

Build and maintain pipelines that process petabytes of raw and semi-raw data, transform large-scale datasets about domains and brands, implement analytics for marketers and SEO analysts, and deploy/manage production systems using Go, ClickHouse, and CI/CD practices.

Build and maintain ETL/ELT pipelines, write SQL and support data modelling, implement data quality checks (SQL, dbt, Python), embed automated testing into pipelines and CI/CD, and collaborate with analysts, engineers, and business teams to improve data quality and testing processes.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional teams.

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

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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.

Build and maintain Python-based data pipelines to extract, clean, transform, and load CRM, Back Office, and Jira financial data. Run monthly invoicing processes, generate client reports and Tableau dashboards, troubleshoot API and data issues, and collaborate with Sales, Account Management, Billing, Legal, and Integration teams to ensure data accuracy and insights.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

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.