Top Data Engineer Jobs in Poland

Develop immersive VR scenes and scenarios for airplane-level design integration reviews. Prepare and optimize CAD, CATIA, and IVT data; administer VR lab hardware and software; facilitate cross-functional engineering reviews; report findings to leadership; provide training and technical support; and improve VR workflows for manufacturing, maintenance, safety, and product design.

Design, build, and optimize Snowflake-based cloud data platforms and scalable ELT/ETL pipelines for financial-services clients. The role involves data integration, modeling, governance, performance and cost optimization, advanced Snowflake capabilities, stakeholder collaboration, technical leadership, mentoring, and delivery of client projects in an Agile environment. The one-year engagement requires relocation to Qatar.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build production-grade agentic workflows for data validation, troubleshooting, and operational automation. Develop reusable frameworks, implement data quality controls and monitoring, optimize workloads, support deployment and production operations, and collaborate with business, engineering, and platform teams.

Identify AI opportunities across business teams and build production-ready AI applications, automations, data pipelines, and clean data models. Develop LLM features such as RAG systems, agents, and assistants with evaluation and monitoring. Support Snowflake and dbt data layers, promote AI adoption through tools and training, and experiment with emerging AI technologies.

Design and maintain a production data lakehouse supporting financial events, including bets, wallet transactions, and live odds. Own bronze, silver, and gold layers; CDC streaming ingestion; schema evolution; optimization; retention and archival; reconciliation; governance; access controls; PII protection; monitoring; and cost management across AWS S3, Snowflake, and Databricks.

Build a reusable Python ingestion framework to replace legacy Java ETL, develop real-time PostgreSQL-to-Snowflake streaming and CDC pipelines, and improve customer onboarding with LLM-assisted schema mapping. Own production deployment, monitoring, data quality, recovery, and secure handling of sensitive data. Collaborate with the Data and AI Platform team to design infrastructure and make pragmatic architectural decisions.

Design, develop, and maintain data pipelines and ETL processes for a predictive jet-engine maintenance system. Process large datasets using Databricks and Spark, write Python data-processing scripts, optimize data platforms, and ensure data quality and performance. Collaborate with technical and business teams in a cloud-based Azure environment.

Design, develop, and maintain cloud-based data pipelines and ETL processes for a predictive jet-engine maintenance system. Process and transform large datasets using Databricks, Spark, Python, and SQL. Collaborate with technical and business teams while ensuring data quality, platform performance, and reliable Azure data services integration.

Design, develop, and maintain cloud-based data pipelines and ETL processes for a predictive jet-engine maintenance system. Process and transform large datasets using Databricks, Spark, Python, SQL, and Azure data services. Collaborate with technical and business teams while ensuring data quality, platform performance, and scalable data processing.

Designs and governs data contracts across the organization, including schemas, formats, validation, versioning, and quality standards. Implements automated schema and data-quality checks, selects contract automation tools, and establishes contract lifecycle processes. Collaborates with product, data engineering, BI, data-owner, and development teams to define requirements, resolve dependencies, and deliver cross-functional data solutions on GCP.

Design, develop, and optimize a high-performance PostgreSQL data platform for financial markets and FX data. Responsibilities include processing approximately 40 million daily messages, managing partitioned large-scale storage, optimizing indexes and queries, maintaining ETL processes, integrating messaging systems, and ensuring near-real-time availability, scalability, and high availability.

Design and maintain scalable cloud-native data platforms and distributed pipelines using Databricks, Apache Spark, AWS, and Azure. Integrate heterogeneous data sources while implementing quality controls, schema validation, lineage, monitoring, and governance. Optimize pipeline reliability, performance, and cloud costs; contribute to CI/CD and infrastructure automation. Collaborate with scientists, analysts, and engineers to support research, analytics, and machine learning workflows in a fusion-energy environment.

Build and maintain Python/PySpark data pipelines using Databricks and Azure. Analyze and debug data-related system bugs, develop data quality solutions, configure customer tenants, and create onboarding tools and documentation. Collaborate with international product and engineering teams, write unit and integration tests, manage code through GitLab and CI/CD, and support scalable data services.

Design, develop, and optimize cloud-based data pipelines using structured and unstructured data across cloud and on-premises environments. Automate testing, security, stability, and performance processes; contribute to architecture decisions and engineering best practices; and collaborate with international teams delivering real-time and batch financial risk analytics solutions.

Develop and maintain scalable batch and near-real-time data pipelines using Databricks, PySpark, SQL, Delta Lake, and Python. Build and automate ETL workflows, support Data Lakehouse solutions, improve data quality and reliability, optimize pipeline performance, and collaborate with engineers, analysts, and stakeholders on large-scale marketing and advertising data platforms.

Build and optimize enterprise-scale data products and pipelines using Spark, Kafka, Delta Lake, and related big data technologies. Design scalable architectures, improve pipeline performance and data quality, develop stream-processing and ETL solutions, and implement monitoring and observability. Collaborate with internal teams to understand data needs, support adoption, and evolve solutions as requirements change. Research and integrate new data-processing tools while influencing data platform architecture.

Designs and maintains scalable GCP cloud platforms for migration, self-service, automation, and AI enablement. Responsibilities include Terraform-based infrastructure as code, CI/CD pipelines, cloud-native architecture, observability, governance, security, cost optimization, and platform reliability. The role collaborates with data engineers, architects, and delivery teams while developing reusable engineering patterns and supporting large-scale enterprise cloud transformation.

Designs and maintains scalable cloud-native data platforms and distributed pipelines using Databricks, Spark, AWS, and Azure. The role focuses on data modeling, quality, lineage, governance, monitoring, performance optimization, CI/CD, infrastructure automation, and reliable support for scientific research, analytics, and machine learning workflows. Collaboration with scientists and engineers is central to translating research requirements into production data solutions.

Designs and operates ETL pipelines migrating legacy SQL Server data to Azure SQL. Responsibilities include schema discovery, data profiling, transformation mapping, validation and reconciliation, migration rehearsals, production cutover, incident resolution, change management, and technical documentation. The role requires collaboration with business analysts and international stakeholders while maintaining data integrity in regulated environments.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse/AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Work with stakeholders to deliver production-ready, well-tested, high-performance data products and support operational monitoring and reconciliation.

Build and maintain Databricks data solutions using PySpark and SQL, create Delta tables and analytical data models, integrate data sources, investigate quality issues, maintain scheduled jobs, improve performance, and translate business requirements into reliable technical solutions. The role also involves documentation, monitoring, data validation, and collaboration with analysts, Product Owners, and international teams.

Design and deliver Azure-based data platforms and pipelines using Databricks, Medallion Architecture, Delta Lake, and dbt. Analyze source systems, data quality, governance, integration requirements, and KPI feasibility across HR, payroll, finance, and operational data. Produce technical designs, standards, estimates, and risk assessments while collaborating with architects, engineers, clients, and business stakeholders through workshops and architecture reviews.

Design, build, and optimize data pipelines and frameworks to ingest data into a Data Lake from sources like Kafka, MQ, SFTP and APIs. Implement testing, ensure data quality, governance, security, and collaborate with analysts and data scientists while researching performance improvements.

Lead migration from SAS/Exadata to a Microsoft Fabric data platform: build and maintain data pipelines, migrate/validate reports and data products, implement data quality, metadata/catalog processes, optimize performance, provide testing, documentation and post-go-live support, and enable knowledge transfer.

Lead migration and modernization of legacy analytics to Microsoft Fabric: design lakehouse architectures, build ingestion pipelines, implement governance, CI/CD and data quality, and deliver enterprise-scale data migrations while liaising with stakeholders.