Collaborate with Molecular Invention (MI) researchers, data scientists, Domain Analysts, BI&T, and cross-functional partners - including research scientists, informaticians, and computational modelers - to gather, model, and integrate diverse structured and unstructured data sources (experiment/study reports, lab instrument data, chemical/protein information, external datasets), delivering high-quality, contextualized, and AI-ready data products. Develop and implement data engineering solutions - including data modeling and entity-relationship structures - to enable scientific workflows, predictive use cases, and advanced analytics. Build, maintain, and enhance data infrastructure, pipelines, and automation processes for the efficient ingestion, cleaning, validation, and analysis of diverse scientific data sources. Implement and oversee rigorous data quality checks, validation processes, and performance monitoring to ensure data accuracy, reliability, and timely delivery of insights. Champion data governance best practices (metadata management, cross-source consistency, FAIR principles, error resolution, taxonomy, and quality control). Support onboarding for new data products and knowledge transfer, maintaining detailed documentation for product designs, processes, usage, and continuity. Advocate adoption of Research Data Ecosystem platforms and advanced analytical/AI tools, connecting technical peers to effective data-driven solutions. Drive innovation by staying current with emerging technologies and industry trends (data engineering, Drug Discovery, GenAI, LLMs) and partnering with Platform teams to enhance data product offerings and operational efficiency. Foster a collaborative, accountable, and innovative technical culture through networking and proactive engagement. Support global collaboration with work hours overlapping research and IT sites in the Western US, Eastern US, Hyderabad, and Bangalore. Supporting People with Disabilities Excellent interpersonal, collaborative, team building, and communication skills to ensure effective collaborations within matrix teams. Ability to work with diverse teams across organizational lines and structures. Proven ability to explain complex analyses and outcomes to both technical and non-technical stakeholders. Desire to work in a fast-paced, dynamic environment. Passion for technology and driving impactful outcomes for patients. Bachelor or graduate-level degree in a pharmaceutically relevant scientific domain, Computer Science, Information Technology, or related discipline or commensurate industry experience. Proven experience (typically 3-5 years) in a data and analytics role, including direct development experience. Experience in data engineering, such as knowledge of SQL and experience with relational databases. Experience building data products a plus. Experience working with Discovery Researchers or platforms relevant to the Life Sciences systems a strong plus Proficiency in programming languages such as Python; and the ability to transition to new languages. Familiarity with cloud & multiverse platforms and AWS-based engineering technologies (Glue, Lambda, CFT, DMS, etc.). Understanding data visualization and analytics concepts and tools like Spotfire. Experience working in an agile development environment such as Scrum or Kanban. Demonstrated ability to manage a backlog, prioritize features, and deliver iterative product value. Experience with GenAI and LLM technologies is a strong plus.