• Design, develop, and optimize high-performance, scalable data platforms, with a strong focus on streaming data processing, large data volumes, and cloud infrastructure • Implement and maintain infrastructure automation and self-service using Terraform, ensuring reliable and repeatable environments for both development and production, and creating robust CI/CD pipelines • Build seamless integrations between Data Cloud and various relational and noSQL OLTP databases • Design and build frameworks and abstractions to accelerate the development of data pipelines while embedding data engineering best practices • Establish and maintain observability, security, and data governance solutions to ensure high quality and reliability of data pipelines

• Lead the iOS core libraries, framework & infrastructure strategy • Build scalable libraries, frameworks & infrastructure • Provide technical leadership to drive architectural decisions • Create a robust and scalable logging framework • Collaborate closely with stakeholders and other teams

• 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.

• Serve as the team's Java subject matter expert, providing technical guidance, code reviews, and architectural input on Java-based components. • Design and implement custom Apache NiFi processors to support UDM data processing and routing requirements. • Lead and assist in the migration of existing analysis pipelines to NiFi-based workflows, ensuring continuity, correctness, and performance parity. • Collaborate with data transport, platform, and infrastructure engineers to align pipeline design with enterprise standards. • Troubleshoot complex integration issues across data pipeline stages, including format conversion, schema validation, and service connectivity. • Contribute to technical documentation, architecture decision records, and pipeline design artifacts. • Mentor junior and mid-level engineers on Java best practices, NiFi patterns, and integration design. • Deliver within an Agile/Scrum framework, actively participating in sprint planning, backlog refinement, and technical reviews.