• Team Development: Lead, mentor, and manage a team of Data Engineers, fostering a culture of ownership, continuous improvement, and technical excellence. • Cross-Functional Partnering: Serve as the primary liaison between the Data Engineering team and other departments (Client Success, Partnerships, Product, Engineering, Clinical, and Business Intelligence) to translate business needs into technical requirements and roadmaps. • Project Management: Work with leadership, stakeholders, and product managers to coordinate roadmap commitments, keep projects on track, and communicate and roll with changes as they inevitably occur. • Process Improvement: Drive the adoption of DevOps and DataOps methodologies, automating workflows, improving deployment processes, and reducing manual operational burden. • Operational Excellence: Oversee daily data operations of our Data Platform, including monitoring, incident response, and performance tuning of data pipelines and databases to ensure high uptime and meet defined SLAs. • Data Quality & Governance: Implement proactive data quality checks and monitoring frameworks. Partner with stakeholders to establish and enforce data governance policies. • HIPAA & Compliance: Ensure all data operations and infrastructure adhere strictly to healthcare regulatory requirements, including HIPAA and other relevant data privacy standards. • Disaster Recovery: Develop and maintain robust backup, recovery, and business continuity plans for critical data assets. • Architect and Build: Lead the design and implementation of scalable, reliable, and performant ETL/ELT data pipelines and data warehouse solutions that meet the demands of a growing organization. • Multi-Cloud Infrastructure: Own and optimize the data infrastructure across AWS and Azure cloud environments, ensuring interoperability, cost efficiency, and robust security. • Technology Expertise: Drive the selection and adoption of best-in-class data technologies, including modern data warehouses (e.g., Snowflake, Azure Synapse), orchestration tools (e.g., AWS Glue, Azure Data Factory), and real-time streaming solutions. • Code Quality: Set and enforce standards for code quality, testing, version control (Git), and documentation for all data engineering projects.

• Design, build, and optimize ETL/ELT workflows using tools like Databricks, SQL, Python/pyspark & Alteryx (Good to have) • Develop and maintain robust, scalable, and efficient data pipelines for processing large datasets • Work on cloud platforms (Azure, AWS) to build and manage data lakes, data warehouses, and scalable data architectures • Utilize cloud services like Azure Data Factory, AWS Glue, or similar for data processing and orchestration • Use Databricks for big data processing, analytics, and real-time data processing • Create and manage SQL-based data solutions, ensuring high availability, scalability, and performance • Collaborate with cross-functional teams to deliver impactful data solutions • Leverage CI/CD pipelines to streamline development, testing, and deployment of data engineering workflows • Maintain clear documentation for data workflows, pipelines, and processes

• Design and implement enterprise-scale data pipelines using Databricks on AWS, leveraging both cluster-based and serverless compute paradigms • Architect and maintain medallion architecture (Bronze/Silver/Gold) data lakes and lakehouses • Develop and optimize Delta Lake tables for ACID transactions and efficient data management • Build and maintain real-time and batch data processing workflows • Create reusable, modular data transformation logic using DBT to ensure data quality and consistency across the organization • Develop complex Python applications for data ingestion, transformation, and orchestration • Write optimized SQL queries and implement performance tuning strategies for large-scale datasets • Implement comprehensive data quality checks, testing frameworks, and monitoring solutions • Design and implement CI/CD pipelines for automated testing, deployment, and rollback of data artifacts • Configure and optimize Databricks clusters, job scheduling, and workspace management • Implement version control best practices using Git and collaborative development workflows • Partner with data analysts, data scientists, and business stakeholders to understand requirements and deliver solutions • Mentor junior engineers and promote best practices in data engineering • Document technical designs, data lineage, and operational procedures • Participate in code reviews and contribute to team knowledge sharing

• Architect complex, high-volume data pipelines for production use. • Design and implement scalable data models serving multiple product and internal teams. • Own data quality frameworks and standards across key data products. • Build reusable patterns for transformations and metrics to drive efficiency. • Define and maintain core business metrics and Key Performance Indicators (KPIs) in partnership with Analytics. • Own the data products used across the company, ensuring reliability and performance. • Set and promote standards for data modeling and pipeline development. • Partner closely with Analytics, Data Science, and Machine Learning teams on requirements to reduce friction and accelerate their work. • Mentor engineers and actively participate in the hiring process.