Top Data Engineer Jobs in Poland

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 and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Build and maintain data warehouse solutions using Snowflake, SQL, dbt, and dimensional modeling. Develop, test, and deploy data models; manage code with Git and Bitbucket; and support CI/CD workflows, containerized deployments, and automation using Python. The role also requires German proficiency and may involve Azure services, orchestration tools, finance data products, and requirements engineering.

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.

Designs, develops, and optimizes scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Builds agentic workflows for data engineering automation, implements data quality and monitoring controls, develops reusable frameworks, optimizes workloads, supports deployment and production operations, and troubleshoots complex data issues.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate data validation, troubleshooting, and operational processes. Develop reusable frameworks, implement data quality controls and monitoring, optimize workloads, support production deployment, and resolve complex data issues while collaborating with business, data engineering, and platform teams.

Develop and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows to automate data validation, troubleshooting, and operational processes. Create reusable frameworks, implement quality controls and monitoring, optimize performance, support production deployment, and troubleshoot complex data issues while collaborating with business and platform teams.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, and operational automation. Implement data quality controls, monitoring, testing, deployment, and production support while creating reusable frameworks and resolving complex data issues.

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

Design, develop, optimize, and support scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows and reusable data engineering frameworks, implement data quality and monitoring controls, troubleshoot production issues, and support testing, deployment, and ongoing enhancement of data and AI-enabled solutions.

Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate validation, troubleshooting, and operational processes. Create reusable frameworks, implement data quality and monitoring controls, optimize workloads, support production deployment, and resolve complex data issues in collaboration with business, data engineering, and platform teams.

Designs, builds, and operates reusable SAP data products using SAP Datasphere and SAP Business Data Cloud. Evaluates extraction and integration approaches, modernizes SAP BW and BW/4HANA data models, and develops future-state data architectures. Collaborates with architects, consultants, business experts, and IT teams while applying expertise in SAP source systems, ABAP, SQL, semantic modeling, data quality, and data harmonization.

Lead the architecture and hands-on development of cloud-native data platforms, real-time and batch pipelines, data products, and scalable storage systems. Work with Databricks, Snowflake, AWS, GCP, Spark, Flink, and related technologies. Provide technical direction, establish engineering standards, mentor engineers, ensure data quality and security, and advise clients on pragmatic platform and architecture decisions.

Design and develop scalable data platforms and pipelines for analytics and operations. Build distributed data workflows with Spark, PySpark, and Databricks; leverage Azure services including Data Factory, Synapse, Event Hubs, and Data Lake Storage; model and integrate data from multiple sources; optimize pipeline performance and reliability; and collaborate with analysts, scientists, and engineers. Improve data engineering standards, testing, orchestration, and operational practices.

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.

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.

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.

Design, build, and maintain scalable data pipelines, models, and backend data infrastructure. Develop dbt transformation workflows on Snowflake or Databricks, manage orchestration, and create internal data applications with tools such as Streamlit. Translate product requirements into technical solutions, collaborate across engineering and analytics teams, and use GenAI tools to accelerate development from design through deployment and continuous improvement.

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.

Designs and maintains ETL/ELT pipelines, data models, and data layers using SQL, Python, PySpark, Databricks, Azure, and Delta Lake. Ensures data quality through validation, monitoring, alerting, and incident diagnosis; optimizes processing performance and costs; documents data processes and lineage; and delivers trusted data for analytics, reporting, automation, and product teams. The role also translates business requirements into technical solutions and responsibly verifies AI-assisted work.

Own end-to-end data readiness across ingestion, validation, transformation, aggregation, extraction, and delivery workflows. Develop and optimize Python, PySpark, SQL, Databricks, ADF, and Delta Lake solutions; investigate data-quality and reconciliation issues; coordinate validation with stakeholders and providers; resolve complex pipeline and platform incidents; review engineering work; guide junior engineers; and maintain documentation, contracts, runbooks, and deployment procedures.

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.