We are currently looking for an experienced Lead Data Engineer to strengthen our engineering unit in Zürich to support our banking client in a hybrid working environment.
In this role, you will be responsible for designing, implementing and maintaining data-driven applications, engineering robust data pipelines and contributing to all stages of the software development lifecycle.
This role focuses on understanding and bridging the architectural and functional gaps between legacy systems and the new data platform. It requires a combination of data engineering expertise and solution architecture capabilities, particularly around lineage, compliance and integration strategy.
Would you like to be a part of this ambitious and innovative project? We look forward to receiving your application.

REQUIREMENTS

• Solid experience in data engineering and data architecture within complex on-premise environments
• Knowledge of Informatica PowerCenter, ETL architecture and integration patterns
• Hands-on understanding of Kafka, S3-based data lakes, Hive Metastore and Postgres
• Deep knowledge of schema evolution, historization and data governance models
• Good knowledge of SQL, Python and data mapping/documentation using industry best practices
• Familiarity with data modeling frameworks, metadata-driven ingestion and common data models
• Working experience in regulated environments (preferably banking or finance)
• Understanding of storage lifecycle and network segmentation (C3/C4)
• Fluent English skill is a must, German language skill is a significant advantage

• Lead and execute Gap analysis between Informatica PowerCenter-based data flows and the new S3/Kafka/Hive platform
• Understand application-level integration, system interfaces and data exchange logic
• Design data ingestion and transformation strategies aligned with metadata, historization and business domains
• Model and document data flows, lineage and mapping from legacy DWH to the new architecture
• Collaborate with business and platform stakeholders to define target state architecture for key use cases
• Support transition of data products, schemas and pipelines into scalable, governed frameworks
• Evaluate opportunities to simplify, consolidate and modernize across data zones and ETL flows
• Recommend future-proof design choices based on architecture, security and platform readiness