Top Data Engineer Jobs in Poland

The Senior Data Engineer will design, build, and maintain data systems, pipelines, and infrastructure for data processing and analysis, collaborating with stakeholders and ensuring data quality.

The role involves designing scalable data pipelines and architectures, integrating multiple data sources, and ensuring data quality and governance within the CMDB and Asset Intelligence platform.

The Senior Data Engineer will design and maintain data systems, build data pipelines, optimize ETL processes, and ensure data quality, collaborating with various teams to facilitate data-driven decision making.

The Data Engineering Lead is responsible for designing and optimizing data architectures, overseeing data systems, mentoring team members, and ensuring high-performance data processes that align with business goals.

Design, build, and maintain scalable data pipelines on GCP; optimize data models; ensure data quality; support legacy data migration; collaborate with teams; implement monitoring; contribute to data governance best practices.

As a Data Engineer, you'll design and maintain scalable data pipelines and analytics solutions in GCP, focusing on LLM usage analytics, data transformation, and collaboration with teams to ensure high-quality data processing.

As a Principal Big Data Engineer, you will design scalable solutions, develop data pipelines, create features, and collaborate with various teams to enhance Akamai's data platform.

As a Data Engineer II, you will design scalable data pipelines in AWS, develop SQL transformations, and enhance MLOps workflows, focusing on analytics and machine learning initiatives.

The Data Engineer will design, build, and maintain data pipelines, manage workflows using Apache Airflow, troubleshoot incidents, and collaborate on data delivery and validation processes.

Design and build scalable Snowflake data models and analytical pipelines using SQL and Python to support actuarial research, translate actuarial concepts into data workflows, optimize performance, and collaborate with actuarial stakeholders.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale transformations, support SQL Server/SSIS platforms, and collaborate with BI teams to enable analytics and reporting.

Design, build, and maintain Azure Databricks data pipelines and Lakehouse architectures using PySpark, Delta Lake, and Azure Data Factory. Integrate data from SQL Server/SSIS, optimize performance for large datasets, ensure data quality, and collaborate with BI teams to support Power BI reporting.

Design, develop, and maintain Azure Databricks data pipelines and Lakehouse architectures using PySpark, Spark SQL, Delta Lake, and Azure Data Factory. Integrate data from SQL Server/SSIS, ensure scalability, data quality, and performance, support Power BI reporting, and help migrate ETL to cloud-based architectures.

Design, build, and maintain scalable data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale data processing, support SQL Server/SSIS environments, and collaborate with BI teams for reporting and analytics.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark/Spark SQL, Delta Lake, and Azure Data Factory. Integrate and optimize data from multiple sources, support SQL Server/SSIS environments, ensure data quality and scalability, and collaborate with BI teams for analytics and reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake and Azure Data Factory. Integrate multiple data sources with SQL Server/SSIS, optimize performance for large-scale processing, and collaborate with BI teams to support reporting and analytics.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale ETL/ELT processes, support SQL Server/SSIS platforms, and collaborate with BI teams to enable analytics and reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and transform data from multiple sources, optimize large-scale processing, support SQL Server/SSIS environments, and collaborate with BI teams to enable analytics and reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale distributed processing, support SQL Server/SSIS platforms, and collaborate with BI teams to enable analytics and reporting.

Design, build, and maintain scalable GCP data pipelines and ETL/reverse ETL processes using Dataflow, Dataform, and BigQuery. Develop secure APIs for ingestion, optimize data models and Datastream integrations, implement monitoring, and ensure GDPR/CCPA-compliant data governance for player analytics and fraud detection.

The Senior AI Data Engineer will lead the development of data infrastructure for AI projects, ensuring scalable data pipelines and governance, while collaborating with the AI team to optimize performance and facilitate AI training and operations.

The Data Engineer develops and maintains data infrastructures and pipelines, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Data Engineer develops and maintains data pipelines and infrastructure, optimizing performance and ensuring data quality while collaborating with teams for data-driven solutions.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

Build and maintain ETL/ELT pipelines, write SQL and support data modelling, implement data quality checks (SQL, dbt, Python), embed automated testing into pipelines and CI/CD, and collaborate with analysts, engineers, and business teams to improve data quality and testing processes.