Description
Join DB Global Technology, Deutsche Bank’s technology center in Central and Eastern Europe. The Bucharest Technology Centre (BEX) delivers global tech products, driving the Bank's transformation.
You will join the OneMIS stream, responsible for management, regulatory & risk reporting, and advanced analytics. Our mission includes enhancing data quality via KPIs and migrating data platforms to modern, cloud-native ecosystems.
We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high‑performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud.
Responsibilities
- Build scalable data pipelines: Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting.
- Develop SQL and BigQuery solutions: Write and optimize advanced SQL transformations and build performant, cost‑efficient BigQuery data models.
- Develop Python workflows: Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high‑quality code.
- Design data models and ensure quality: Build robust data models and apply validation practices to maintain accuracy and reliability.
- Build cloud‑native data solutions: Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms.
- Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance.
- Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards.
Skills
- 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems.
- Strong expertise in SQL and hands‑on experience building performant datasets in BigQuery (or similar cloud data warehouses).
- Proven experience with Python and PySpark for scalable data processing in distributed environments.
- Solid understanding of data modeling, ELT/ETL patterns, and data quality best practices.
- Experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services.
- Hands‑on experience building scalable data pipelines (batch and near real‑time) in a cloud‑native environment.
- Proficiency with version control, CI/CD pipelines, and automated testing frameworks.
- Ability to troubleshoot and optimize performance across compute, storage, and processing layers.
- Proven ability to leverage AI tools to enhance productivity, optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs.
Benefits
- Emotionally and mentally balanced: Support for dealing with life crises, maintaining stability through illness, and maintaining good mental health.
- Physically thriving: Private healthcare and life insurance with premium benefits, healthier ways of working, and check-up's.
- Socially connected: Hybrid working model with flexible working options, retailer discounts, cultural and CSR activities, workshops, and more.
- Financially secure: Competitive income, performance-based promotions, meal vouchers, bonuses for referrals.