• Define and maintain enterprise data architecture standards across structured, semi-structured, and unstructured data domains. • Use AI-assisted modeling tools to accelerate data model design and validate designs against business requirements. • Design and govern the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS. • Establish data ontology, taxonomy, and semantic layer standards that enable AI systems to reason over organizational data accurately. • Evaluate emerging data architecture patterns and build a roadmap for their adoption across Stellus Rx. • Design scalable data models and ELT/ETL pipeline architectures that support analytics and AI/ML workloads. • Define standards for data partitioning, indexing, caching, and storage optimization. • Partner with Data Engineers to translate architectural blueprints into production-ready pipelines. • Define and enforce data governance frameworks, data quality standards, and data contracts across the enterprise. • Develop and maintain a master data management (MDM) strategy.

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery of large datasets across diverse industries. • Implement and ensure data privacy and security best practices, supporting data sovereignty and compliance with regulatory requirements. • Collaborate closely with AI/ML engineers, Data Scientists, and Platform engineers to enable advanced analytics and AI capabilities while retaining strict data control. • Optimize data platforms and systems for performance, reliability, and cost efficiency. • Build tools and frameworks for secure, privacy-preserving data processing and orchestration. • Develop and maintain documentation, data models, and technical workflows. • Partner with cross-functional teams to launch new data-driven product features and solutions.

• 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.