Timisoara Data Engineer Databricks

Job description

Data Engineer - Databricks Publication Date: Sep 27, 2026 Ref. No: 523741 Location: Timisoara, RO #job-location Who we are. We are a team of passionate experts with a clear ambition: applying digital technology to advance what matters for our clients and society. Together we create reliable and responsive digital foundations for the world’s businesses, institutions, and communities. With a 10 year history in Romania and over 2200 employees, Atos has a presence in 3 of the largest cities in Romania: Bucharest, Brasov and Timisoara. Our Global Delivery Center in Romania offers cutting-edge, best in class, end-to-end IT services for more than 300 clients located around the world. Learn more on Advancing what matters We are looking for a Data Engineer with deep expertise in Databricks to design, build, and scale our data platform. You will own the architecture and performance of large-scale data pipelines, drive best practices across the Lakehouse stack, and partner closely with Data Science, Analytics, and AI Engineering teams to deliver reliable, production-grade data infrastructure. This role is ideal for someone who has moved beyond writing pipelines to also shaping platform standards, mentoring engineers, and making architectural decisions on a Databricks-based Lakehouse. Key Responsibilities Design, build, and optimize scalable ETL/ELT pipelines on Databricks , processing large volumes of structured and unstructured data Architect and maintain Delta Lake -based data models, ensuring data quality, reliability, and performance across Bronze/Silver/Gold layers Develop and orchestrate workflows using Databricks Workflows , Delta Live Tables (DLT) , and job scheduling tools Implement data governance, access control, and lineage using Unity Catalog Optimize Spark jobs for performance and cost (cluster sizing, partitioning, caching, Photon, Z-ordering, file compaction) Build and maintain CI/CD pipelines for Databricks notebooks/jobs (Databricks Asset Bundles, Repos, Git integration) Collaborate with Data Scientists and ML Engineers to support feature engineering and MLflow -based model lifecycle workflows Define and enforce data engineering best practices: testing, version control, observability, and documentation Monitor pipeline health and cost, implementing alerting and proactive optimization (cluster policies, job clusters vs. all-purpose clusters) Mentor mid-level engineers and contribute to technical decision-making and platform roadmap Ensure compliance with data security and privacy requirements (e.g., GDPR)