Data Engineer
HybridParis, Île-de-France, France
Paris, Île-de-France, FranceHybridFull TimeData ManagementSmall
Full TimeSmallData Management
Job Summary
Ingest and onboard datasets from internal systems, APIs, databases, and real-time feeds into scalable ETL/ELT pipelines using Python, PySpark, and SQL. Build and operate batch and stream refreshes on Palantir Foundry and Databricks while modeling data to support analytics and operational applications. Ensure data trust through testing, quality checks, observability, and lineage in collaboration with analysts and product teams. Deliver trusted, well-documented datasets with meaningful monitoring and alerting within the first six months.
Required Qualifications
- 1-3 years in data engineering or analytics engineering with end-to-end pipeline delivery in production
- Proficiency in Python & PySpark for distributed data processing
- Strong SQL for analytical and transformation logic
- Data modeling skills for both analytics and operational use cases
- Experience with data ingestion from APIs, databases, external feeds, and real-time sources
- Solid grasp of data quality, testing, observability, lineage, and governance practices
- Comfort working with large datasets and distributed compute using modern ELT patterns
Desired Qualifications
- Databricks or cloud-native compute with compute pushdown
- Palantir Foundry: pipelines/transforms, Code Repos, Ontology, and operational applications
- Spark execution concepts: partitions, shuffles, caching, and performance optimization
- Experience with financial or enterprise operational data
- Experience with AI-assisted ETL/ELT or data quality tooling
- Familiarity with streaming frameworks and/or orchestration tools
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