Top Data Engineer Jobs in Poland

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

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 and operate production-grade batch and real-time data pipelines on GCP (BigQuery, Airflow, Kafka), ensure data quality via contracts and observability, develop connectors and integrations, support ML feature pipelines, and mentor junior engineers while contributing to engineering standards and CI/CD practices.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Develop, operate, and optimize a multi-tenant Azure data platform using Data Factory, ADLS Gen2, Databricks, and Synapse Analytics. Build scalable PySpark and Spark ETL/ELT pipelines, maintain data infrastructure, implement CI/CD automation, optimize cost and performance, and support data governance, security, modeling, and reporting. Collaborate with BI and data teams across development, test, and production environments.

Build and maintain scalable data pipelines and warehousing to support ML and LLMs. Assist in model development, deployment, data transformation, quality, and performance optimisation, collaborate with data scientists, document workflows, and evaluate new AI technologies.

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.

Design, build, maintain, and troubleshoot data pipelines and processing systems for production and analytics. Improve data quality, drive optimization and tooling, and collaborate with engineers, ML experts, and stakeholders in agile teams while engaging with clients.

Lead architecture and technical standards for a greenfield, enterprise-scale data platform supporting analytics, operational data products, and AI workloads. Design Iceberg-based event flows, canonical data models, batch and near-real-time pipelines, governance, data contracts, quality, lineage, observability, tenant isolation, and deployment automation. Optimize scalability and costs while mentoring engineers and partnering with leadership and cross-functional stakeholders.

Support building and maintaining ETL/data pipelines in Azure/Databricks, prepare and transform large datasets, write SQL, create simple Python scripts, monitor data quality, troubleshoot basic issues, and collaborate on data analysis.

Support building and maintaining ETL data pipelines in Azure/Databricks, prepare and transform large datasets, write SQL, create simple Python processing scripts, monitor data quality, troubleshoot issues, and collaborate with the team on data analysis.

Own and operate the ClickHouse data warehouse and ETL pipelines; build and maintain dashboards and BI layer; provide proactive analyses (cohorts, unit economics, attribution); implement monitoring/alerting; work closely with co-founders and cross-functional leaders to make data the single source of truth.

Lead a data engineering team while designing and implementing scalable data solutions, pipelines, models, and quality processes. Support forecasting, machine learning, web application, optimization, and analytics platforms. The role includes technical leadership, people management, troubleshooting, documentation, coding standards, and collaboration with data scientists, AI engineers, DevOps engineers, architects, and platform teams.

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.

Build, maintain, debug, migrate, and automate data pipelines and infrastructure in an AWS and infrastructure-as-code environment. Create workflows and jobs, update AWS services, perform regression testing, review colleagues’ code, and collaborate with developers and data engineers. The role uses Python, SQL, Terraform, Docker, Glue, Athena, PySpark, Pandas, and related data technologies, with occasional international travel and office attendance in Katowice or Gdańsk.

The AI Data Engineer will design and implement workflows for automating data reporting, using AI to enhance the telemedicine platform's analytics.

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 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.

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.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

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 develop databases, data lakes, and data ingestion pipelines, ensuring scalable solutions and engaging with stakeholders across various departments.