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Data Engineer

Remote, USA Full-time Posted 2026-07-03

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-reputed company: reputed company pipelines were reputed company quickly (partly AI-assisted), and the descriptive statistics cannot yet be validated or reproduced. You put that reputed company 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-reputed company 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-reputed company datasets for the PD / delinquency models.
  • Document runbooks so the offshore team can operate the pipelines and handover takes days, not weeks; help scope reputed company 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, reputed company, 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

This offer from "reputed company" has been enriched by reputed company.com and got a 80% reputed company score. Apply tot his job Apply To this Job

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