SENIOR DATA ENGINEER

Senior data engineer to design and operate production ETL/ELT pipelines on enterprise cloud platforms (GCP and Azure) for healthcare data. The role owns pipelines end-to-end — from requirements through deployment and ongoing operations — and is expected to set technical direction when requirements are unclear, not wait for it. Core Responsibilities Design, build, and operate scalable ETL/ELT pipelines across GCP, Azure, BigQuery, and SQL Server. Model data for analytical workloads — dimensional modeling, SCDs, and schema design. Orchestrate pipelines using Airflow, Cloud Composer, Azure Data Factory, or similar. Handle PHI in line with HIPAA requirements — secure movement, de-identification, access controls, and audit. Deploy via Git and CI/CD; monitor and maintain pipelines in production. Translate stakeholder needs into technical plans; communicate feasibility and tradeoffs early. Technical Requirements 7+ years in data engineering, with significant time at senior level delivering production systems. Strong SQL plus Java or Python for pipeline development. Hands‑on across multiple cloud stacks (e.g., GCP, Azure, BigQuery, SQL Server). Deep experience designing and operating reliable, scalable ETL/ELT pipelines; performance‑minded. Hands‑on with at least one orchestration tool (Airflow, Cloud Composer, ADF, Dagster, or Prefect). Strong data modeling skills — dimensional modeling, normalization, slowly‑changing dimensions. Working knowledge of HIPAA and PHI handling — secure movement, de‑identification, access controls, audit. Track record delivering on enterprise cloud platforms within standards and controls. Proficient with Git and CI/CD. Other Requirements End‑to‑end ownership: Drives delivery from definition through production with minimal direction; prioritizes operational delivery, not just prototypes. Business translation: Converts stakeholder needs into clear technical plans; communicates feasibility and value early. Proactive exploration: Evaluates new tools, runs lightweight proofs of concept, and shares findings. Industry awareness: Tracks modern data trends (semantic layers, LLM‑assisted dev, modern ELT) and brings relevant insights back. Continuous learning: Adapts quickly while maintaining consistent delivery. #J-18808-Ljbffr