Top Data Engineer Jobs in Poland

The Data Engineer develops ETL/ELT pipelines and data warehousing solutions, collaborating with teams to enhance data capabilities at UL Solutions.

The GIS Data Engineer supports developing GIS data pipelines, performs geoprocessing tasks, conducts validation, and collaborates with teams for reliable GIS data delivery.

As a Data Engineer at Zapier, you'll build scalable data systems, enhance product functionality through data, and collaborate with teams to improve data access and usability.

As a Data Engineer, build and manage data pipelines, ensure data quality, and support analytics for trading. Collaborate with teams to define data needs and best practices for infrastructure and applications.

The role involves developing innovative Data & Analytics products, enhancing existing solutions, and creating Tableau dashboards while ensuring data integrity and compliance in collaboration with finance stakeholders.

The Data Engineer will develop and optimize data pipelines, improve architectural solutions, and collaborate across teams to deliver data-driven insights.

The Data Engineer will design and maintain data pipelines and infrastructure, support analytics and automation, and collaborate on ML/GenAI projects.

The Senior QA Engineer will execute manual and automated tests, collaborate on requirements, support UAT, and ensure software quality for financial systems.

As a Senior/Expert ML/Data Engineer, you will build data pipelines and ML models, focusing on AI/ML innovations, mentoring, and optimizing data solutions.

The Data Engineer will develop and maintain ETL/ELT pipelines, implement monitoring systems, establish data quality checks, and document data structures.

As a Junior Data Engineer, you will support the development of data pipelines, maintain data quality processes, and collaborate with stakeholders to ensure reliable data infrastructure.

As a Data Engineer, you'll maintain and configure the Snowflake data platform, design integration pipelines, and innovate with new tools. Your role supports data security and performance while collaborating across teams.

The Junior Data Engineer will analyze data, design experiments, build a marketing data stack, and collaborate with teams to improve data access and insights.

As a Full Stack Engineer, you will develop features for the analytics platform using Java and TypeScript, focusing on backend services and React apps, enhancing the software observability product.

Design and implement customer data integrations, optimize SQL for analytics, and collaborate with teams to ensure data integration success.

As a Junior/Mid-Level ML Data Engineer, you'll develop data pipelines, support ML model deployment, and collaborate with teams on AI projects.

As a Data Engineer II, you'll design and maintain data infrastructure, build data pipelines, and ensure data quality and performance while collaborating with cross-functional teams.

Design and automate data pipelines, develop systems for storing operational data, ensure data quality, and collaborate with data scientists.

Develop reusable data pipelines, optimize ETL processes, build data integrations, and improve data platform architecture while collaborating with cross-functional teams.

As a Data Engineer, you'll design, develop, and maintain BI solutions, ensuring data quality and performance while collaborating with business partners.

Design and implement data solutions, optimize ETL pipelines, and lead teams in creating scalable data architectures using Microsoft Fabric.

The Data Engineer will design and implement data pipelines, databases, and processing systems, ensuring data security and integrity while collaborating with stakeholders.

The role involves designing and maintaining data pipelines, applying DevOps principles, managing cloud resources, and collaborating with teams on documentation and architecture.

As a Middle Data Engineer, you will support the migration of SQL Server data to Snowflake, refactoring ETL processes, ensuring data movement efficiency for analytics, and collaborating with teams for QA and deployment.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.