Data Architect – SDTM

Title: Clinical Data Business Analyst Duration: 12 Months Contract, with extension Location: Remote but Travel to Alameda CA (1 week/month) Role Overview This role is the connective tissue between Biometrics and Data Engineering. It exists because clinical data has meaning that structure alone doesn t carry: someone has to understand what the data represents clinically, what the standards require of it, and how it must therefore be modeled and then make sure the engineering team builds systems that produce it correctly. The SME owns that translation. They bring clinical data domain expertise and a solid working command of CDISC standards, and they use it to guide the design of data products that are clinically correct, standards-conformant, traceable, and ready to support analysis and an AI-ready data strategy. Working knowledge of CDISC, SDTM, and ADaM is mandatory specifically, at the level required to design systems and pipelines that enforce the standards, map source data into them, and extend them where the data demands it. Mandatory CDISC / SDTM / ADaM (applied) Solid working knowledge of CDISC, sufficient to design systems and pipelines that produce conformant data.Demonstrated source-to-SDTM mapping: the ability to take raw, EDC, or legacy clinical data and map it correctly into target SDTM domains.Practical command of controlled terminology enough to design validation that enforces it systematically, not to author it.Ability to extend or customize SDTM domains where the data requires it, with the judgment to know when that s warranted versus when existing domains or supplemental qualifiers are the right answer.Working understanding of ADaM and how analysis-ready data derives from SDTM, sufficient to guide design and preserve traceability.Familiarity with what conformance and validation expectations mean in practice, and with the SDTM Implementation Guide as a working reference. Also Required Clinical data domain expertise a real understanding of how clinical data is collected and why that shapes how it must be modeled.Hands-on familiarity with EDC systems and the realities of clinical data collection.A demonstrated ability to gather clinical and business requirements and convert them into technical specifications and user stories engineering teams can build from.Experience partnering with data engineering and architecture teams in a supporting, advisory capacity comfortable guiding design without taking over implementation.Working knowledge of clinical data workflows, external data integration, data standards, and data governance in a regulated (GxP / 21 CFR Part 11) environment.Strong communication and stakeholder management skills, and fluency in both directions able to hold a conversation with Biostatistics, Statistical Programming, Clinical Operations, and Regulatory on one side, and data engineers and architects on the other. Required Education & Qualifications Bachelor s degree in Computer Science, Information Systems, Data Science, Software Engineering, Statistics, Applied Mathematics, Bioinformatics/Health Informatics, or a related technical or quantitative discipline.Equivalent demonstrated experience in data architecture, data modeling, or data engineering will be considered in place of a formal degree. What matters is the ability to reason rigorously about data structure, relationships, and integrity however that capability was acquired. Preferred Master s degree in a data-oriented field Data Science, Analytics, Computer Science, Information Systems, Biomedical/Health Informatics, or Biostatistics.Formal coursework or credentials in data modeling, data architecture, or database design.Certification in a relevant data discipline:Data management or governance (e.g., CDMP / DAMA-certified data management professional)Data architecture or solution architectureCloud data platforms (e.g., AWS, Azure, or equivalent data-focused certification)Business analysis (e.g., CBAP/CCBA) or an equivalent requirements-engineering credentialCDISC training or certification.