• 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.

• Lead a team of strong engineers building high-scale, highly reliable data processing systems serving the Ads domain • Build a strong team vision and roadmap for the team • Partner with stakeholders to enable collaboration among teams • Provide direct, constructive feedback grounded in empathy • Monitor and proactively address productivity and efficiency • Hire and grow a diverse, high-performing team

• Drive the design, development, and governance of data integrations across HR systems • Set integration standards and collaborate with engineering partners • Build robust, reusable, well-governed data pipelines and APIs • Develop full-scale applications when needed • Serve as thought leader and trusted advisor for data integration and architecture

• Lead and deliver modern data platforms and analytics initiatives for global clients. • Blend hands-on data engineering, solution architecture, and client advisory responsibilities. • Work closely with business and technical stakeholders to design scalable, secure, and high-performance data solutions.