• Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets (CSV, Parquet, JSON). • Lead efforts to canonicalize raw healthcare data (837 claims, EHR, partner data, flat files) into internal models. • Own the full lifecycle of core pipelines — from file ingestion to validated, queryable datasets — ensuring high reliability and performance. • Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with SMEs, Account Managers, and Product to ensure successful implementation and troubleshooting. • Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability. • Refactor and scale existing pipelines to meet growing data and business needs. • Tune Spark jobs and optimize distributed processing performance. • Implement schema enforcement and versioning aligned with internal data standards. • Collaborate deeply with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, SMEs, and AMs to ensure pipelines meet evolving business needs. • Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues. • Contribute to the evolution of our data platform — driving toward mature patterns in observability, testing, and automation. • Build and enhance streaming pipelines (Kafka, SQS, or similar) where needed to support near-real-time data needs. • Help develop and champion internal best practices around pipeline development and data modeling.