Data Engineer

Role Summary

• Build and operate large-scale healthcare data pipelines across batch workflows, metadata-driven ingestion, and data service publishing.
• Own end-to-end engineering from source ingestion to conformed data products, with strong focus on reliability, data quality, and operational observability.
• Partner with analytics, business, and platform teams to deliver trusted datasets for sales, claims, activity, patient, and rare disease use cases.

Key Responsibilities

• Design and maintain PySpark/SQL pipelines in Databricks for landing, unified, unstitched, and published data layers.
• Build and support Airflow DAGs for scheduling, dependencies, retries, and production operations.
• Implement metadata/config-driven frameworks for ingestion, transformation, and rule-based processing.
• Develop robust data quality controls, DQ summaries, failure handling, and alerting workflows.
• Manage batch/process audit logs, run status tracking, release flags, and operational reporting.
• Integrate multi-source data (files, APIs, cloud storage, and relational systems) into governed Delta/Spark tables.
• Optimize pipeline performance using partitioning, parallelization, and query tuning.
• Collaborate on schema evolution, business-rule onboarding, and production support.

Required Skills

• Bachelor’s degree in Computer Science, Information Technology, or a related field with 5-9 years of experience 
• Advanced Python, PySpark, and SQL (window functions, complex joins, MERGE patterns, optimization).
• Hands-on Databricks and Airflow experience in enterprise environments.
• Experience with cloud data platforms (AWS), object storage, and secure secret handling.
• Strong data quality engineering, monitoring, and troubleshooting in regulated data contexts.
• Solid understanding of ETL orchestration, dependency management, and SLA-driven delivery.