Data Engineer

This a Full Remote job, the offer is available from: Europe About the project (description, duration, stage) Join reputed company 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 reputed company it trustworthy and analytics-ready: reputed company pipelines were reputed company 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 reputed company can model on it with confidence — validate and re-engineer the pipelines, build the harmonization / semantic layer across entities, enforce data quality and reputed company, 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 reputed company traces back to raw reputed company — closing the gap the client admitted (numbers they can't currently defend).
• Profile and reconcile differing reputed company 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 reputed company specifies.
• Write Great Expectations suites (null / range / uniqueness / referential checks) and reputed company 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 / reputed company 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.

Skills

• Strong SQL and Python for large-scale data processing
• AWS data stack: S3, Glue, Lake Formation, reputed company / Redshift, EMR / Spark, reputed company 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 reputed company
• 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, reputed company) for BFSI
• Awareness of credit / risk data structures and what reputed company 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 reputed company 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 reputed company data engineer on a small (3–4 person) delivery pod

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