• Design, build, and maintain scalable, reliable data pipelines using Python and Apache Spark to support data science and ML workflows. • Architect and own the data platform infrastructure—including data lakes, data warehouses, and feature stores—ensuring performance, quality, and governance at scale. • Partner closely with data scientists and ML engineers to build and maintain the data foundations required for model training, validation, and deployment. • Define and implement data engineering best practices including pipeline orchestration, data quality frameworks, lineage tracking, and observability. • Lead the design of reusable data assets—feature engineering pipelines, curated datasets, and domain-specific data models—that accelerate ML experimentation and production readiness. • Collaborate with platform and DevOps teams to operationalize data infrastructure through CI/CD pipelines, infrastructure as code, and automated testing. • Evaluate and introduce modern data tooling and frameworks, driving continuous improvement in the data engineering ecosystem. • Establish and enforce data governance, security, and compliance standards aligned with HIPAA and healthcare data requirements. • Conduct design reviews and technical mentorship for senior and mid-level data engineers across the organization. • Serve as a cross-functional technical authority, aligning data engineering direction with product, clinical, and analytics stakeholders.