Top Data Engineer Jobs in Poland

Build, operate, and maintain batch and real-time data pipelines using Azure, Databricks, Python and streaming tech (Kafka, Spark, Flink). Ingest and validate time-series and event data, ensure data quality, lineage and monitoring, and collaborate with Data Scientists, ML Engineers and platform teams to enable forecasting and real-time analytics.

Design, build and maintain scalable data pipelines and transformations using Microsoft Fabric, Spark/PySpark, Python and SQL. Implement Lakehouse/OneLake and Fabric Data Warehouse solutions, integrate multiple data sources, support CI/CD with Azure DevOps, ensure data quality, performance and security, and create semantic models and Power BI reports while collaborating with business and technical stakeholders.

Build and maintain scalable data pipelines and ingestion workflows using Microsoft Fabric and Azure. Develop transformations with Spark/PySpark, Python and SQL; design OneLake/Lakehouse and data warehouse solutions; create semantic models and Power BI reports; support CI/CD and deployments; ensure data quality, performance, security, and stakeholder collaboration.

Design, build and maintain scalable data pipelines and transformations using Microsoft Fabric, Azure and Spark. Create semantic models and Power BI reports, support CI/CD, ensure data quality, security and collaborate with stakeholders to deliver data platform solutions.

Design and deliver end-to-end Microsoft-based data architecture solutions (Fabric, ADF, ADLS, Synapse). Define capacity, performance, security, governance, integration architecture, and data modeling standards.

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.

Design and deliver end-to-end Microsoft-based data architecture solutions (Fabric, ADF, ADLS, Synapse). Define capacity planning, performance optimization, governance, security frameworks, data modeling standards, and oversee system integrations to build scalable lakehouse and data warehouse platforms.

Build and maintain scalable data pipelines and ingestion workflows using Microsoft Fabric and Azure. Develop transformations with Spark/PySpark, Python and SQL, create semantic models and Power BI reports, integrate multiple data sources, support CI/CD and ensure data quality, performance, scalability and security while collaborating with business and technical stakeholders.

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.

Design and deliver end-to-end Microsoft-based data architecture solutions including lakehouse and data warehouse patterns. Define capacity planning, performance optimization, data modeling standards, security and governance frameworks, and integration architecture. Support business initiatives and mentor teams to build a scalable data platform.

Design and deliver end-to-end data architecture on Microsoft platforms. Define capacity planning, performance optimization, data modeling standards, security and governance frameworks, and integration architecture to build scalable lakehouse and data warehouse solutions using Microsoft Fabric and Azure services.

Design and deliver end-to-end Microsoft-based data architecture solutions (Microsoft Fabric/Azure), define capacity and performance strategies, set data modeling and governance standards, design security and integration frameworks, and support scalable lakehouse/data warehouse platforms.

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.

Design and build reusable ETL/ELT pipelines and integrations for Autodesk platforms, prototype AI-enabled workflows (LLMs, agents, MCP), produce demos and feasibility studies, validate integrations, document reusable implementation patterns, and collaborate with product, security, and consulting teams to operationalize customer-facing data solutions.

Design and implement scalable backend solutions for collecting and managing large volumes of data across Veeam’s cloud, SaaS, virtualization, backup, and replication products. Develop features, own major components, contribute to architecture, define requirements with product management, and estimate implementation effort, costs, and capacity. The role requires strong C# and .NET expertise, software architecture experience, problem-solving skills, and effective communication in English.

Design, build and maintain scalable data pipelines and Lakehouse/Warehouse solutions using Microsoft Fabric and Azure. Develop transformations with Spark/PySpark, Python and SQL, create semantic models and Power BI reports, support CI/CD and ensure data quality, performance, scalability and security while collaborating with stakeholders.

Design and implement backend systems, ETL pipelines, data storage, and APIs for a SaaS platform. Maintain platform availability and improve security, quality, testing, automation, documentation, and features. Support customers and staff, contribute to sprint planning and prioritization, estimate development effort, track project progress, and proactively resolve roadblocks.

Maintain, optimize, and modernize large-scale data pipelines, models, workflows, and transformation processes. Improve SQL performance, reliability, scalability, and processing latency while identifying technical gaps and architectural opportunities. Collaborate with product engineering teams, support platform evolution, and drive initiatives from concept through implementation in a high-autonomy cybersecurity environment.

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.

Develop feature stores and other data services, build ELT and ETL pipelines, integrate data-processing and storage tools, and support scalable, high-load systems. The role requires expertise in Python, SQL, event-driven systems, streaming pipelines, system design, APIs, message brokers, and cloud data technologies. It is fully remote and involves operational data-engineering tasks.

Build and operate PostgreSQL change-data-capture pipelines into Azure using Kafka Connect and Debezium. Deploy and scale stateful connector workloads on AKS, automate onboarding and recovery with Python, integrate streams with Azure data services, and manage infrastructure as code. Monitor production pipelines, troubleshoot failures and performance issues, and apply data protection controls including masking, hashing, access control, and retention. The role requires hands-on Kafka, CDC, PostgreSQL, Python, Azure, Kubernetes, and production platform experience.

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.

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.