Data Engineer | JPC

Key ResponsibilitiesDesign, build, and maintain production ETL/ELT pipelines integrating laboratory and operational systems (e.g., Darwin, Teamcenter/PLM, LabVantage LIMS, Jama, TurboAC, and Qdocs/Veeva) into Microsoft Azure Fabric Lakehouse and PostgreSQL. Develop and optimize Bronze, Silver, and Gold medallion architecture, including schema mapping, data modeling, referential integrity, and performance optimization. Build scalable API integrations and cross-cloud data pipelines across Azure and AWS to support enterprise data integration. Implement automated data quality controls, controlled vocabulary normalization, schema validation, Q-gate/specification checks, data lineage, and audit trails to ensure GxP compliance. Monitor, troubleshoot, and optimize pipeline performance, reliability, error handling, and operational monitoring in production environments. Collaborate with business, engineering, and IT teams to integrate data sources and establish a governed, scalable digital thread supporting analytics, AI, and regulatory reporting. Desired Background5+ years of hands-on Data Engineering experience building production ETL/ELT pipelines and API integrations. Expert-level Python and/or PySpark for data ingestion, transformation, orchestration, testing, and CI/CD. Experience with Microsoft Azure Fabric (Lakehouse, Data Factory, Fabric Pipelines, Delta Lake) delivering end-to-end production solutions. Hands-on AWS experience, including S3, Glue (or equivalent), and RDS/Aurora. Strong SQL and PostgreSQL experience, including normalized schema design, query optimization, indexing, and performance tuning. Experience with data modeling, medallion architecture, and Lakehouse design patterns. Experience implementing data lineage, quality controls, schema validation, error handling, and monitoring in regulated environments. Knowledge of GxP, GMP, GCP within pharmaceutical, biotechnology, or medical device environmentsSCM, WMS, PLM, MM, QA, any validated applications with in a FDA Regulated systems