Top Data Engineer Jobs in Poland

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.

Build scalable analytics pipelines using modern data technologies, define data models, optimize systems, and collaborate with teams for data needs.

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

The Data Engineer will design scalable data pipelines, collaborate with teams on data capabilities, and integrate AI/ML models while optimizing existing systems.

We are seeking a Mid QA Engineer to ensure software quality through testing and collaboration in the banking sector, requiring 4+ years experience.

Develop scalable software, collaborate on design and architecture, conduct code reviews, and mentor new team members in a high-performance environment.

The Senior Data Engineer will maintain, extend, and improve data/ML workflows, collaborate with teams to enhance the data platform, and build datasets on demand.

The role involves managing data infrastructure and telemetry systems, supporting data transfer and storage, and developing ETL processes for game analytics.

The Data Engineer will develop data infrastructure and models for Waymo's commercialization team, focusing on data warehousing and pipeline solutions, collaboration with teams, and ensuring data quality and compliance.

As a Data Engineer, you will build scalable data systems and tools to enhance developer productivity at Netflix, collaborating with cross-functional teams and mentoring junior talent.

The Data Engineer will design, maintain, and optimize data pipelines and architectures, ensuring data quality and collaborating with cross-functional teams for analytics solutions.

The Data Engineer will construct data pipelines, manage ETL processes, optimize SQL queries, and ensure data integrity, utilizing various tools such as Airflow, Python, and SQL.

The Principal Data Engineer at Moderna will design and implement data architecture solutions using AWS, mentor teammates, and work on analytics initiatives to improve business outcomes.

Seeking a Data Engineer to manage ETL processes, maintain data warehouses, and optimize data workflows in Google Cloud, primarily using Apache Airflow and SQL.

The AI/NLP/Data Engineer will develop ML and AI models, enhance NLP systems, and build supporting infrastructure while collaborating with teams on deployment and documentation.

The Data Engineer will design and deploy data pipelines, manage data models, and support business intelligence activities within Gett's platform.

The Staff Data Engineer will architect and build data and MLOps platforms, managing the full data lifecycle and implementing best practices for data governance and model versioning.

As a Data Engineer, you will design scalable data pipelines, collaborate with teams, optimize systems, and support AI/ML integration.

Design, develop, and maintain data pipelines; optimize data processes; lead junior engineers; address data-related issues.

As a Data Engineer, you will develop and support big data processing pipelines, build cloud-based infrastructure, and provide visualization solutions for data assets.

Design and build data pipelines and systems that support AI-driven insights and analytics, collaborating with cross-functional teams.

The Data Platform Engineer will develop and maintain data lakes and pipelines, propose improvements, and ensure best practices in data engineering.

The Data Engineer will migrate legacy data pipelines into the Snowflake and dbt environments, developing Python-based ETL flows and collaborating with cross-functional teams to ensure a scalable data platform.

Design and maintain data pipelines using DBT, optimize models in Snowflake, and develop Streamlit applications with Python while applying software development best practices.

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