• Design and evolve a scalable, secure, cloud-native lakehouse platform leveraging Databricks, Microsoft Fabric (OneLake, Lakehouse, Data Factory), and dbt. • Define modeling patterns, governance frameworks, and engineering best practices across the data lifecycle. • Lead design reviews and guide teams in adopting scalable architectural patterns. • Drive long-term platform strategy and evaluate emerging technologies. • Design and implement batch and streaming data pipelines for healthcare data sources (EHR, claims, HL7/FHIR, APIs, flat files, databases). • Develop modular ingestion, quality, lineage, metadata, and observability frameworks that scale across domains. • Produce clean, analytics-ready datasets and data models for BI, analytics, and machine learning workloads. • Implement HIPAA-aligned access patterns and secure handling of PHI. • Architect Databricks workloads (clusters, jobs, Unity Catalog, Delta Lake) for reliability, performance, and cost efficiency. • Integrate Databricks and Microsoft Fabric with Azure services and enterprise systems. • Partner with product managers, data scientists, analysts, clinicians, and business stakeholders to translate healthcare data needs into scalable solutions. • Lead cross-functional initiatives that modernize and unify the organization’s data ecosystem. • Mentor senior and mid-level engineers; elevate team capability through technical coaching and standards. • Drive roadmap planning, platform evolution, and long-term data strategy. • Champion engineering excellence, reliability practices, documentation quality, and governance.

• Own the technical strategy and execution of migrating large-scale data workloads from GCP to AWS, ensuring continuity, data integrity, and minimal disruption. • Design migration playbooks and serve as the go-to expert for decisions across compute, storage, and orchestration layers during the transition. • Architect and implement scalable batch and streaming data pipelines using Apache Spark, Delta Lake, and the medallion architecture. • Establish standards for pipeline design, data quality, and observability that the broader engineering organization can build on. • Take accountability for the reliability, performance, and cost-efficiency of production ETL jobs running on AWS (EMR, Glue) against terabyte-scale datasets. • Proactively identify and address bottlenecks, technical debt, and opportunities to improve throughput and resilience.

• Design, build, and own scalable data platforms on Google Cloud • Play an architectural role, driving end-to-end data solutions • Define best practices and mentor team members • Work closely with stakeholders, analysts, and data scientists to deliver reliable, high-performance data pipelines and analytics platforms

• Build and maintain data infrastructure that enables the collection, storage, and retrieval of data; • Create new data flows by integrating our data sources and ensuring they are reliable and efficient; • Develop ETL pipelines, data warehousing, and data modeling to support business needs; • Ensure data quality monitoring, reliability, and lineage by developing processes and tools to identify and correct data quality issues; • Collaborate with other members of the Data & Analytics Team to optimize the data infrastructure and improve data governance; • Provide documentation and training to end-users on data sources, pipelines, and data quality procedures; • Stay current with the latest technologies and techniques related to data engineering, and identify opportunities to improve data infrastructure and analysis.