Top Data Engineer Jobs in Warsaw

Design and build scalable batch and streaming data pipelines using Databricks, Apache Spark, PySpark, Spark SQL, and Delta Lake. Develop Lakehouse and Medallion architecture data models, integrate cloud and enterprise data sources, optimize large-scale SQL workloads, and implement data quality, testing, CI/CD, DevOps, and DataOps practices. Collaborate with architects, analysts, and data scientists to deliver production-grade data products. The role is hybrid in Poland and requires travel to client locations across Europe.

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

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.

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

Build and maintain scalable data pipelines and integrations for a centralized data platform. Develop shared components, CI/CD pipelines, and Infrastructure as Code solutions while ensuring data quality, reliability, and consistency. Contribute to architecture decisions, engineering standards, code reviews, documentation, and system integrations. The role requires strong Python, SQL, Spark, ETL/ELT, data modeling, orchestration, monitoring, and relational database skills, with opportunities to work with streaming and lakehouse technologies.

Own the financial audit data workstream for Printful and Printify. Deliver accounting reconciliations, investigate transaction discrepancies, build Snowflake transformations, extend Looker models, collaborate with Accounting and auditors, and document pipelines, corrections, and edge cases against fixed deadlines.

Build and operate production data pipelines, dbt models, Airflow DAGs, and AWS infrastructure as code. Own ingestion, transformation, orchestration, monitoring, data quality, observability, and CI/CD. Support AI-ready data initiatives and Redshift access, collaborate with stakeholders, and provide end-to-end ownership from requirements through troubleshooting. The role requires strong Python, SQL, AWS, analytical data modeling, infrastructure-as-code, and autonomous problem-solving skills.

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.

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.

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.

Build compliant, high-quality real-time data pipelines for CDR, network telemetry, and customer data. Enable Kafka and Flink streaming for AI use cases, implement data quality, lineage, and governance controls, manage feature engineering pipelines for machine learning models, and ensure compliance with data privacy regulations. Success is measured through real-time readiness, data quality, and pipeline reliability.

Design, build, and maintain batch engines and data marts for credit risk (PD, EAD, LGD). Implement regulatory-compliant pipelines, liaise with analysts and modelers, manage incidents and priorities, and contribute to digitalization and tooling for the Risk domain.

Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.

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.

Designs, builds, and supports GCP-based data ecosystems, pipelines, storage architectures, and data models. The role gathers requirements, works directly with clients, optimizes queries and infrastructure, troubleshoots high-impact data issues, monitors system performance, and implements data integration and transformation solutions. It also involves mentoring junior data engineers and collaborating with cross-functional teams and distributed stakeholders.

Design, own, scale, and iterate data processing pipelines for Mapbox Roads. Implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate in a cross-functional team to build navigation and high-definition mapping features.

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.

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

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

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.