Data Engineer – Build Reliable Data Pipelines

About the role

This position is for a Data Engineer based in Switzerland, working within a regulated credit and lending environment. You will transform a newly centralized data lake into a robust, analytics‑ready foundation that supports data science and risk‑modelling use cases.

Key responsibilities

• Rebuild and validate data pipelines to ensure reproducibility of reporting and statistics.
• Profile, reconcile, and harmonize heterogeneous source schemas into a unified data model.
• Design and implement dbt‑based models (staging, intermediate, marts) with extensive testing.
• Develop data quality frameworks using Great Expectations and dbt tests.
• Implement entity resolution and record linkage across fragmented customer and account data.
• Ensure anonymization and pseudonymization meet regulatory requirements.
• Optimize large‑scale Spark jobs for performance and cost efficiency.
• Orchestrate production pipelines with Airflow or AWS Step Functions.
• Deliver clean, documented, feature‑ready datasets for downstream teams.
• Create technical documentation and runbooks for operational handover.

Required profile

• 4+ years of professional data‑engineering experience.
• Strong exposure to large‑scale AWS and Spark environments.
• Experience in regulated financial or risk‑modelling contexts.
• Proven ability to design reliable, auditable data pipelines.

Required skills

• AWS (S3, Glue, Athena, Redshift, EMR, Step Functions)
• Apache Spark
• SQL
• Python
• dbt
• Great Expectations
• Airflow
• Data governance, lineage, and reproducibility concepts