About the project (description, duration, stage)

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Join Neurons Lab as a Data Engineer on a new engagement with a regulated UK & Ireland credit and lending company. The client has lifted data from multiple business entities into a newly centralized, anonymized data lake, but lacks the data-engineering depth to make it trustworthy and analytics-ready: current pipelines were assembled quickly (partly AI-assisted), and the descriptive statistics cannot yet be validated or reproduced.

You put that foundation on solid ground so the Data Science Lead can model on it with confidence — validate and re-engineer the pipelines, build the harmonization / semantic layer across entities, enforce data quality and lineage, and prepare clean, feature-ready datasets.

This is a foundational data-engineering role on a regulated data estate; data protection and reproducibility are the primary constraints on every decision.

Full-time engagement preferable.





What you'll actually do (example tasks)
- Reproduce a descriptive-statistics report end-to-end so any figure traces back to raw source — closing the gap the client admitted (numbers they can't currently defend).
- Profile and reconcile differing source schemas across acquired entities: map differing field names, types, encodings and business definitions for the same concept into one conformed model.
- Build dbt staging intermediate mart models with tests; codify the harmonized definitions the Data Science Lead specifies.
- Write Great Expectations suites (null / range / uniqueness / referential checks) and wire them into the pipeline so bad data fails loudly rather than silently corrupting analysis.
- Implement entity / identity resolution (deterministic + fuzzy matching) where there is no clean shared key for the same customer or account across sources.
- Implement and verify anonymization / pseudonymizat

📌 Data Engineer (Madrid)
🏢 Neurons Lab
📍 Madrid