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Implements and configures an AI platform by integrating customer data, building ETL workflows, troubleshooting pipelines, managing recurring data deliveries, and automating manual processes. The role collaborates with customers and internal teams to define technical requirements, resolve implementation issues, deploy tailored solutions, and maintain data security and privacy.

Build and operate full-stack product capabilities using React, TypeScript, backend services, APIs, cloud infrastructure, and data systems. Responsibilities include implementation, testing, deployment, monitoring, incident learning, and technical design. The role partners with product, design, and ML teams to turn life sciences data and AI capabilities into accessible customer experiences, while using coding AI agents and modern delivery practices.

Own complex customer data implementations from technical discovery through production go-live. Build automated ETL workflows, scalable data ingestion solutions, validation frameworks, and data-quality processes using Python, SQL, PySpark, Snowflake, and AWS. Troubleshoot integrations and production issues, ensure compliance with security and privacy requirements, mentor junior engineers, and improve implementation standards and tooling while working directly with technical and non-technical customer stakeholders.

Own full-stack product capabilities from problem framing through deployment and iteration. Build React and TypeScript user experiences, AWS Lambda computation services, scalable APIs, and backend systems across AWS, Kubernetes, Snowflake, PostgreSQL, DuckDB, and DataFusion. Lead technical design, code reviews, architecture discussions, testing, observability, and mentoring while partnering with product, design, ML engineering, and leadership on roadmap decisions.