Data Engineer

• 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.
• 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.

Do you have experience in Spark?, * 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, * 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, * 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