{"id":3926984,"url":"https://alion.io/job/metlife-data-engineer","title":"Data Engineer","company":{"id":450553,"name":"MetLife","domain":"metlife.com","url":"https://alion.io/company/metlife-5","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Informatica","optional":false},{"name":"Oracle ERP","optional":false},{"name":"pySpark","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Azure Data Factory","optional":true},{"name":"Azure SQL Database","optional":true},{"name":"CI/CD","optional":true},{"name":"Git","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-07T17:29:10Z","employer_posted_date":null,"last_verified_at":"2026-11-07T17:29:10Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Key Responsibilities\n\n Design, develop, test, and deploy scalable pipelines using Databricks, PySpark, Spark SQL, and Delta Lake, spanning Bronze/Silver/Gold (medallion) layers for HR data integration and analytics.\n Build data models – logical, physical, dimensional, and integration – and maintain source-to-target mappings for workforce, organization, employee, position, job, and related HR domains.\n Migrate legacy GDC integrations by re-engineering Informatica, SQL, and stored-procedure logic into modern Databricks patterns, including inbound Oracle HCM feeds and outbound HR data files.\n Validate output parity through reconciliation, parallel runs, and source-to-target checks, and build automated data quality, exception handling, and audit controls into pipelines.\n Apply enterprise governance, security, privacy, and role-based access expectations to sensitive HR data, capturing lineage and metadata where applicable.\n Produce production-ready documentation – technical designs, runbooks, data dictionaries, and support handoffs – for maintainable, observable, cost-aware solutions.\n Collaborate within a distributed delivery team, participate in agile ceremonies, mentor junior engineers, and communicate progress, blockers, and risks clearly.\n\nRequired Qualifications\n\n Bachelor's degree in Computer Science, Engineering, or related field, with 7+ years of data engineering, ETL, or data platform experience.\n Azure Databricks & Apache Spark: 3+ years hands-on building Delta Lake pipelines with PySpark, Spark SQL, and SQL across a medallion architecture.\n Data modeling & integration: Strong logical/physical/dimensional modeling plus source-to-target mapping, reconciliation, and production cutover support.\n AI-assisted engineering: Proficiency using AI/Copilot tooling to accelerate development, code conversion, and documentation.\n Practical, delivery-focused work style, comfortable turning ambiguous legacy logic into clean target-state designs within a highly governed environment.\n Strong written and verbal communication, with the ability to overlap U.S. Eastern Time for key meetings, design reviews, and issue resolution.\n\nPreferred Qualifications\n\n Suggested 3+ years modernizing legacy ETL (Informatica-to-Databricks) or building metadata-driven, configuration-driven data frameworks.\n Azure ecosystem: Data Lake Storage, Data Factory, DevOps, Azure SQL/Synapse, Unity Catalog, and Git-based CI/CD.\n HR domain data: Oracle HCM Cloud extracts/PVOs/HDL, workforce analytics, and consumption via Power BI, Crunchr, or similar.\n Experience supporting regulated, sensitive employee data in insurance, financial services, or HR technology.\n Detail-oriented, collaborative, and comfortable working independently against offshore delivery expectations while staying aligned to MetLife 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