Top Data Engineer Jobs in Warsaw

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.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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, build and maintain Snowflake-based data warehouses and dbt models; write advanced SQL; automate pipelines with CI/CD and container concepts; use Git workflows; perform data processing with Python. Provide German-language support (minimum B2).

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.

Design, build, and maintain Azure Data Factory pipelines and ETL/ELT processes; monitor and optimize ADF; develop Python components (Azure Functions, API integrations); support Azure SQL/Synapse warehousing and occasional Power BI reporting; contribute to Azure DevOps CI/CD and collaborate with Product Owner and Architect to deliver scalable data platform improvements.

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 and maintain scalable HR data solutions using Databricks, Spark, SQL and Python. Improve data quality, support migrations/integrations, implement validation and governance, and translate business requirements for HR, Finance, and country stakeholders.

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

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.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

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

Design, build and support scalable, event-driven data ingestion and transformation pipelines in AWS. Develop Kafka/MSK and Glue-based ETL/ELT flows, ensure data quality, implement metadata-driven solutions, support CI/CD and operational monitoring, and collaborate with architects and domain teams to onboard data into the Operational Data Store.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings. Ingest and normalize code dependency graphs into Neo4j and vector indexes in Qdrant, transform source code into structured Markdown/JSON, monitor pipeline runs, debug errors, and collaborate with senior engineers to optimize retrieval and pipeline efficiency.

Build, test, and maintain data pipelines that process AST outputs, knowledge graphs, and vector embeddings; ingest and normalize code dependency graphs into Neo4j and Qdrant; transform source metadata into structured Markdown/JSON assets; monitor pipeline execution and resolve batch errors; collaborate with senior engineers and AI/ML engineers to optimize retrieval and pipeline efficiency.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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

Design, build, and support integrations between manufacturing equipment, MES, historians, ERP, LES, and data platforms to ensure compliant data collection, transformation, storage and transfer. Enable Industry 4.0 and advanced analytics, drive standardization of data models and interfaces, produce validation deliverables and documentation, and collaborate with manufacturing, quality, automation and digital teams.

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

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

Design and implement scalable batch and streaming data pipelines on Databricks and cloud platforms; build data transformations, CI/CD and MLOps practices; collaborate with data scientists; create data governance, monitoring, and Power BI visualizations; translate business requirements into performant technical solutions.

Design, build, and maintain scalable data pipelines and platforms for batch and streaming data using Spark, Airflow, Iceberg, and cloud services. Optimize ingestion, processing, and storage, collaborate with stakeholders, monitor platform performance, and write high-quality Python/Java/Scala code to support enterprise reporting and analytics.

Design, build, and maintain scalable data pipelines; integrate diverse data sources into warehouses or lakes; collaborate with data scientists and engineers to ensure quality and availability; optimize schemas, models, and performance; implement governance, security, and compliance; monitor reliability and document systems.

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.