• Utilize consulting and technical skills to be able to work in a client-facing project environment independently. • Be responsible for your own execution and sometimes lead individual work streams on client engagements as assigned and under supervision of engagement lead. • Collaborate with other team members to successfully deliver on projects. • Work effectively and directly communicate with both internal and client and/or partner teams. • Develop full ownership of your execution on client engagements, you'll become involved in the project planning and solution stages of engagements as well. • Design and implement complex ETL/ELT pipelines with evidence of improved data processing times. • Successfully lead small data warehousing projects with measurable performance enhancements under the management of an engagement lead. • Contribute to real-time data processing solutions and managed streaming data. • Implement security and compliance measures for data pipelines. • Design and implement version control and branching strategies and integrate them into CI/CD for promoting and testing in higher environments.

• Own the Data Products technical roadmap, canonical data modeling, and ingestion scalability. • Manage and optimize the clinical data ingestion pipeline. • Oversee the scaling of customer data onboarding processes and the continuous improvement of data hygiene, canonical storage standards, and observability and monitoring of the data pipelines. • Enable shared funnel visibility across the organization and support the development of a robust enterprise reporting framework. • Drive the adoption of an AI-powered Software Development Life Cycle (SDLC), leveraging AI tools to accelerate coding, testing, and deployment processes while maintaining stringent code quality and security standards.

• Own the Data Products technical roadmap, canonical data modeling, and ingestion scalability. • Manage and optimize the clinical data ingestion pipeline. • Oversee the scaling of customer data onboarding processes and the continuous improvement of data hygiene, canonical storage standards, and observability and monitoring of the data pipelines. • Enable shared funnel visibility across the organization and support the development of a robust enterprise reporting framework. • Drive the adoption of an AI-powered Software Development Life Cycle (SDLC), leveraging AI tools to accelerate coding, testing, and deployment processes while maintaining stringent code quality and security standards.

• Define and drive the long-term data engineering strategy for the Subscriptions User Understanding domain, identifying opportunities that improve scalability, reliability, and business impact. • Design scalable batch and streaming data platforms that power analytics, experimentation, machine learning, and subscriber experiences. • Partner with Product, Engineering, Data Science, Analytics, and Platform teams to turn complex business challenges into durable, well-designed data solutions. • Lead architectural decisions and establish engineering best practices for data quality, governance, observability, reliability, and operational excellence across multiple squads. • Design data models and platform architecture that support sustainable growth toward one billion users while balancing performance, cost, and maintainability. • Simplify complex systems, reduce technical debt, and improve the developer experience across the broader data ecosystem. • Mentor senior engineers, influence technical direction through collaboration, and help foster a culture of thoughtful engineering, knowledge sharing, and continuous improvement.