{"schemaVersion":"jobsearcher.job.v1","id":"0e69da4b40332092126b4f4e","url":"https://jobsearcher.com/jobs/0e69da4b40332092126b4f4e","canonicalUrl":"https://jobsearcher.com/jobs/0e69da4b40332092126b4f4e","title":"Data Engineer","description":"About The Project (description, Duration, Stage)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. 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. SkillsStrong SQL and Python for large-scale data processingAWS data stack: S3, Glue, Lake Formation, Athena / Redshift, EMR / Spark, Step Functions / AirflowData modeling & semantic layer (dbt or equivalent); dimensional modelingEntity resolution / record linkage across heterogeneous sourcesData-quality & testing frameworks (Great Expectations, dbt tests) and data lineageAnonymization / pseudonymization techniques and their analytical trade-offsBig-data processing (Spark) with performance and cost optimization at scaleClear written / verbal English; documents for handover and works well with a distributed teamKnowledgeGDPR fundamentals as applied to anonymized / pseudonymized financial data and UK / EU data residencyAWS Well-Architected (Analytics, Security) for BFSIAwareness of credit / risk data structures and what downstream modeling consumers need — a plusExperience4+ years in data engineering, with strong AWS + Spark / SQL at scaleDemonstrated experience harmonizing / integrating data across multiple source systemsExperience building validated, reproducible pipelines in a regulated environment (BFSI, healthcare, government) — strong plusComfortable stepping into a messy, partly-built data estate and bringing it up to standardComfortable as the sole or lead data engineer on a small (3–4 person) delivery pod","company":"Neurons Lab","rawCompany":"neurons lab","city":"Macedonia","state":"IL","isRemote":false,"isActive":false,"createdAt":"2026-06-21T08:29:57.857Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Engineer","description":"About The Project (description, Duration, Stage)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. 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. SkillsStrong SQL and Python for large-scale data processingAWS data stack: S3, Glue, Lake Formation, Athena / Redshift, EMR / Spark, Step Functions / AirflowData modeling & semantic layer (dbt or equivalent); dimensional modelingEntity resolution / record linkage across heterogeneous sourcesData-quality & testing frameworks (Great Expectations, dbt tests) and data lineageAnonymization / pseudonymization techniques and their analytical trade-offsBig-data processing (Spark) with performance and cost optimization at scaleClear written / verbal English; documents for handover and works well with a distributed teamKnowledgeGDPR fundamentals as applied to anonymized / pseudonymized financial data and UK / EU data residencyAWS Well-Architected (Analytics, Security) for BFSIAwareness of credit / risk data structures and what downstream modeling consumers need — a plusExperience4+ years in data engineering, with strong AWS + Spark / SQL at scaleDemonstrated experience harmonizing / integrating data across multiple source systemsExperience building validated, reproducible pipelines in a regulated environment (BFSI, healthcare, government) — strong plusComfortable stepping into a messy, partly-built data estate and bringing it up to standardComfortable as the sole or lead data engineer on a small (3–4 person) delivery pod","datePosted":"2026-06-21T08:29:57.857Z","dateModified":"2026-06-21T08:29:57.857Z","hiringOrganization":{"@type":"Organization","name":"Neurons Lab","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Macedonia","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0e69da4b40332092126b4f4e"},"url":"https://jobsearcher.com/jobs/0e69da4b40332092126b4f4e"}}