Top Data Engineer Jobs in Poland

Maintain, enhance, and modernize scalable data processes and pipelines. Analyze existing data tables, workflows, and jobs; improve reliability, performance, and architecture; and identify technical risks and inefficiencies. Collaborate with product engineering teams to implement improvements, align architectures, adopt best practices, and execute both short- and long-term data modernization initiatives independently.

Designs, builds, and maintains scalable Python services, engineering tools, and data pipelines. Responsibilities include data extraction and transformation, Airflow orchestration, Snowflake and dbt development, CI/CD, containerization with Docker and Kubernetes, system design, testing, monitoring, documentation, and AI-assisted engineering practices. The role collaborates with engineering and data teams to deliver reliable solutions and improve developer productivity.

Design, develop, and optimize GPU- and CPU-accelerated image and data processing libraries. Implement and performance-tune parallel numerical algorithms, benchmark across architectures, contribute to technical roadmaps, provide technical leadership, and collaborate with product and engineering teams to meet feature and performance requirements.

Develop and enhance software for data logging, measurement-data processing, and visualization. Responsibilities include requirements analysis, software design, module development, testing, user and product support, engineering problem solving, and coordination of software project activities. The role involves collaboration with global teams, ASAM data standards, communication protocols, quality processes, and continuous technical innovation.

Owns production data integrations for an AI-assisted KYC and customer due diligence platform. Responsibilities include mapping data sources, defining data contracts and canonical models, building connectors, integrating Databricks with SQL Server and legacy systems, supporting APIs and SFTP workflows, implementing data-quality checks and contract tests, and validating reliable production data flows.

Lead architecture, migration, and optimization of production data pipelines using Airflow, Spark, dbt, BigQuery, and Python. Establish engineering standards, testing and verification processes, incident response practices, and documentation. Refactor pipelines while ensuring identical outputs, validate data lifecycle rules, resolve production issues, and mentor engineers. Experience with encryption, compliance, security controls, or Kafka is advantageous.

Design, build, and maintain ELT/ETL pipelines into Snowflake, produce governed Power BI analytics, implement data quality/observability, optimize data models for performance and cost, and use AI tools to accelerate development while collaborating across product and finance teams.

Design, build, and maintain ELT/ETL pipelines into Snowflake, model data for analytics, deliver Power BI reports, embed data quality and observability, and use AI tools to accelerate development while collaborating across engineering, product, and finance.

Design, deploy, and operate highly available database infrastructure across multi-cloud, virtualized, and Kubernetes environments. Migrate database services from virtual machines to Kubernetes, support disaster recovery and backup strategies, tune performance, and implement observability, logging, and alerting. Collaborate with engineering teams to resolve infrastructure challenges and automate workflows using scripting and infrastructure orchestration tools.

Build and operate production-grade Kafka-compatible streaming systems and Apache Flink applications. Develop Go services, manage event processing, state, checkpoints, schemas, and delivery guarantees, and deploy workloads on AWS and Kubernetes. Troubleshoot distributed production systems while improving reliability, scalability, performance, and resilience. Collaborate with engineers on platform evolution and apply AI-powered development tools and GenAI workflows.

Designs and operates secure AWS environments, infrastructure as code, CI/CD pipelines, and scalable data lake platforms for Roche’s OneLake initiative. The role uses Python and Boto3 to build automation, APIs, synchronization layers, and serverless solutions, while implementing AWS governance, access controls, and least-privilege security. Responsibilities include Apache Iceberg, AWS Glue, S3, and Snowflake integrations, production troubleshooting, root-cause analysis, stakeholder collaboration, and mentoring.

Build and optimize scalable data pipelines and products supporting marketing budget decisions and attribution. Use Python, SQL, and Airflow while ensuring data quality through validation, monitoring, and documentation. Collaborate with product and marketing stakeholders, participate in code reviews, investigate data anomalies, and explain insights to nontechnical audiences. Work as an independent B2B contractor based in Poland.

Design and maintain data processing pipelines and ETL workflows for a banking platform. Integrate APIs, databases, S3 buckets, SFTP servers, and other systems; transform, validate, synchronize, and test data; troubleshoot production integration issues; and collaborate with stakeholders and developers.

Design and scale cloud-native data platforms for a fusion energy company. Build near-real-time event-driven pipelines, optimize Spark and PySpark workloads, establish lakehouse standards, and improve data quality, observability, testing, and CI/CD. Develop self-service tooling and microservices, drive AI adoption, manage infrastructure costs, and collaborate with scientific and engineering teams using Databricks and AWS.

Develop and maintain scalable Credit Risk data pipelines and products using SQL, Python, Databricks, and cloud platforms. Modernize legacy SAS processes, onboard source data, and support migration to cloud architecture. Responsibilities include data transformation, validation, reconciliation, governance, documentation, troubleshooting, automation, and analysis across complex datasets. The role collaborates with business, technology, and data governance teams to deliver trusted data solutions for regulatory, forecasting, modeling, and reporting needs.

Build, maintain, and optimize ETL processes, data pipelines, and Teradata data warehouse solutions for banking applications, regulatory reporting, risk, and analytics. Responsibilities include data validation, profiling, reconciliation, performance tuning, data quality, compliance, governance, metadata management, and collaboration with business and technology stakeholders.

Design and scale cloud-native data platforms for a fusion energy company. Build near-real-time event-driven pipelines, modernize Spark and PySpark workloads, establish lakehouse standards and data contracts, and improve testing, CI/CD, observability, reliability, and cost efficiency. The role also develops self-service tooling and microservices, supports AI and agentic workflows, and collaborates with scientific and engineering teams using Databricks and AWS.

Design, build, and manage scalable Databricks data solutions, including batch and real-time ETL/ELT pipelines. Implement Delta Lake, Medallion Architecture, data quality frameworks, CI/CD, governance, lineage, and data mesh practices. Optimize Spark workloads, manage integrations, support data migrations, and collaborate with cross-functional international teams to deliver reliable data for analytics and business intelligence.

Lead the design and hands-on development of scalable Java/Spring Boot backend services, data integrations, APIs, microservices, and distributed event-driven applications. Provide technical guidance, conduct design and code reviews, mentor engineers, and ensure engineering standards for reliability, performance, security, testing, CI/CD, and observability. Collaborate with frontend teams and support AI-enabled data capabilities, including retrieval workflows and intelligent automation.

Own and operate a business-critical data platform, developing SQL databases, data models, integrations, pipelines, and automated data quality controls. Build trusted datasets and semantic layers for Power BI reporting, maintain documentation and lineage, manage access and versioning, troubleshoot failures, and collaborate with business and technical stakeholders. Continuously improve automation, governance, performance, and reporting accuracy across sustainability and operational data use cases.

Lead the architecture and development of scalable data pipelines, production datasets, and cloud data infrastructure. Manage schema evolution, backfills, performance optimization, incident response, observability, and reliable customer-facing reporting. Partner with Data Science, Analytics, and Product teams while mentoring engineers and establishing best practices for code quality, testing, security, and system design.

Build and optimize data models, integrate data from databases, APIs, streaming platforms, and files, and maintain reliable, cost-efficient pipelines and storage. The role involves troubleshooting data quality issues, reducing technical debt, and partnering with business stakeholders to translate requirements into effective data solutions. Strong SQL, Python, cloud platform knowledge, and fluency in Polish and English are required. The position is based in Warsaw with three office days per week.

Develops in-vehicle data quality checks, validation-drive automation, data insights, ETL solutions, APIs, data pipelines, and processing tools for autonomous driving systems. Collaborates with engineering, product, data science, and domain teams while adapting big-data technologies for cloud platforms. Requires Python, C++ or Rust, SQL, scripting, machine learning, data preparation, statistics, and debugging expertise. Experience with cloud, containers, embedded AI, automotive datasets, Git, CI/CD, and ISO 26262 is beneficial.

Builds data engineering solutions for in-vehicle data quality, validation-drive automation, data triaging, and insights supporting hands-off urban driving. Responsibilities include developing APIs, cleaning and organizing raw and processed data, implementing analytics and machine learning solutions, and collaborating with engineering, product, and domain teams. The role involves cloud-based ETL and big-data systems connected to embedded driver-assistance technologies.

Develops and optimizes BigQuery datasets, views, and queries; builds and maintains GCP ELT/ETL pipelines; models analytics data; defines quality checks, lineage, and documentation; collaborates with stakeholders on technical requirements, testing, and releases; and ensures secure, reliable operations through IAM, monitoring, incident response, and automation.