Top Data Engineer Jobs in Warsaw

Design, build, and maintain backend services and automation for data pipelines and internal tooling. Integrate AI/ML features, own CI/CD and observability, lead code reviews, and collaborate with cross-functional teams to ensure scalable, reliable production systems.

The Junior Big Data Engineer will assist in building and maintaining scalable data pipelines in a supportive team, focusing on ETL workflows and collaborating with data scientists.

The Data Quality and Governance Developer will develop and implement data quality rules, support the JLL Data Quality Program, and resolve production issues. Responsibilities include data profiling, code reviews, and participation in agile methodologies.

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.

As a Data Engineer, you will develop and maintain ETL data pipelines, ML data pipelines using Temporal, and backend services in Go while ensuring high performance and reliability.

The Data Engineer will focus on infrastructure automation, reliability, and cost optimization. Responsibilities include designing Infrastructure-as-Code, managing CI/CD pipelines, establishing observability standards, and collaborating with teams to improve platform resilience.

Develop backend services using Kotlin or Java, process data with Scala and BigQuery, design data pipelines, support AI/ML model development, and collaborate on data-driven applications.

As a Big Data Engineer, you will design and implement data processing systems, manage technologies like Hadoop and Spark, and build ETL processes to ensure data quality and performance.

The Senior Data Engineer will design and optimize ETL/data pipelines, automate workflows, integrate data sources, and translate business needs into data solutions using Azure tools.

The Senior Data Analyst will build and maintain marketing data models, design analytical datasets, implement SQL transformations, and ensure data quality while collaborating with technical and business teams to support decision-making.

As a Senior Data Engineer, you will design, implement, and maintain real-time data pipelines and cloud-based infrastructure, mentor engineers, and collaborate across teams to enhance data products and services.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Design and deploy scalable database solutions, migrate services to Kubernetes, and manage high-availability infrastructure for Telnyx's cloud platform.

As a Staff Data Engineer, you'll design and implement distributed data processing systems, build data pipelines, and optimize performance using various technologies.

The Senior Data Engineer will build and optimize data pipelines, collaborate with engineering teams, and ensure data quality and scalability.

Develop and enhance BI solutions using SAP technologies, ensuring best practices in performance, data integration, and training users on SAP Data & Analytics.

The role involves leading the design and development of enterprise security products, focusing on architecture, engineering, and mentorship in data and SaaS security.

The Senior Data Engineer will redesign and simplify the data warehouse infrastructure, improve system stability, optimize data pipelines, and oversee infrastructure performance. Responsibilities include conducting audits, making architectural proposals, and supporting ML/AI workflows.

Design and maintain data pipelines and ML systems, implement MLOps practices, and collaborate with teams to address client challenges in AI and data projects.

Design and maintain data pipelines, collaborate with stakeholders, ensure data quality, deploy infrastructure as code, and optimize reporting 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.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.