Data Engineer

About the project (description, duration, stage)Inscríbase rápido, consulte la descripción completa desplazándose hacia abajo para conocer todos los requisitos de este puesto.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 / pseudonymization (hashing / tokenization / k-anonymity) and evidence that re-identification risk is controlled for the client’s IT / compliance team.- Optimize Spark / Glue jobs over tens of millions of rows - partitioning, file formats (Parquet), incremental loads, cost control.- Orchestrate with Airflow / Step Functions; build repeatable, scheduled pipelines rather than one-off scripts.- Prepare clean, documented, feature-ready datasets for the PD / delinquency models.xqbhyrx- Document runbooks so the offshore team can operate the pipelines and handover takes days, not weeks; help scope onboarding of the remaining (Ireland + additional) sources.Skills- Strong SQL and Python for large-scale data processing- AWS data stack: S3, Glue, Lake Formation, Athena / Redshift, EMR / Spark, Step Functions / Airflow- Data modeling & semantic layer (dbt or equivalent); dimensional modeling- Entity resolution / record linkage across heterogeneous sources- Data-quality & testing frameworks (Great Expectations, dbt tests) and data lineage- Anonymization / pseudonymization techniques and their analytical trade-offs- Big-data processing (Spark) with performance and cost optimization at scale- Clear written / verbal English; documents for handover and works well with a distributed teamKnowledge- GDPR fundamentals as applied to anonymized / pseudonymized financial data and UK / EU data residency- AWS Well-Architected (Analytics, Security) for BFSI- Awareness of credit / risk data structures and what downstream modeling consumers need - a plusExperience- 4+ years in data engineering, with strong AWS + Spark / SQL at scale- Demonstrated experience harmonizing / integrating data across multiple source systems- Experience building validated, reproducible pipelines in a regulated environment (BFSI, healthcare, government) - strong plus- Comfortable stepping into a messy, partly-built data estate and bringing it up to standard- Comfortable as the sole or lead data engineer on a small (3-4 person) delivery pod

Strong SQL and Python for large-scale data processing

• AWS data stack: S3, Glue, Lake Formation, Athena / Redshift, EMR / Spark, Step Functions / Airflow
• Data modeling & semantic layer (dbt or equivalent); dimensional modeling
• Entity resolution / record linkage across heterogeneous sources
• Data-quality & testing frameworks (Great Expectations, dbt tests) and data lineage
• Anonymization / pseudonymization techniques and their analytical trade-offs
• Big-data processing (Spark) with performance and cost optimization at scale

• Clear written / verbal English; documents for handover and works well with a distributed team Knowledge

• GDPR fundamentals as applied to anonymized / pseudonymized financial data and UK / EU data residency
• AWS Well-Architected (Analytics, Security) for BFSI

• Awareness of credit / risk data structures and what downstream modeling consumers need - a plus Experience

• 4+ years in data engineering, with strong AWS + Spark / SQL at scale
• Demonstrated experience harmonizing / integrating data across multiple source systems
• Experience building validated, reproducible pipelines in a regulated environment (BFSI, healthcare, government) - strong plus
• Comfortable stepping into a messy, partly-built data estate and bringing it up to standard
• Comfortable as the sole or lead data engineer on a small (3-4 person) delivery pod