Clinical Data SME; Biometrics & Data Engineering

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-conform ant, 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.

This is worth being precise about, because it defines who fits.

What we need: someone who can look at source data and design the system that gets it correctly into SDTM — mapping source to target domains, enforcing controlled terminology through validation rather than manual review, and extending or customizing domains where the data genuinely requires it. This is applied, systems-oriented CDISC expertise: knowing the standard well enough to build against it and to tell an engineering team what “conform ant” actually means in code.

What we don’t need: someone to author the standards themselves. This role doesn’t define new controlled terminology, sit on standards bodies, or own submission deliverables. It consumes the published standards and makes sure our systems honor them.

Owns the clinical meaning of the data. Serves as the authority on what the data represents — how it’s collected, what the clinical workflows behind it imply, and what constraints that places on how it can be structured. This is the judgment that engineering teams cannot supply for themselves.

Designs how the standards get enforced. Specifies how source and EDC data maps into SDTM target domains, and how controlled terminology is validated in the pipeline rather than checked after the fact. Determines when data fits an existing domain, when supplemental qualifiers suffice, and when a domain needs to be extended or customized to represent the data honestly — then makes sure that logic is built consistently rather than reinvented per study.

Translates in both directions. Turns clinical and business needs into requirements, mapping specifications, and user stories engineering can act on — and explains technical constraints and trade‑offs back to Biometrics stakeholders in terms they can decide on.

Protects traceability. Ensures an unbroken path from source data through SDTM to analysis, so any value can be explained and defended. In a regulated environment this isn’t documentation hygiene; it’s what makes the data defensible.

Integrates the data that doesn’t arrive neatly. Brings external sources — labs, PK, biomarkers, imaging, eCOA — into standardized structures, resolving the mismatches in keys, terminology, and timing these sources invariably introduce.

Guides delivery without owning the build. Works alongside data engineers and architects, supplying clinical and standards input, reviewing designs, and validating that what’s delivered means what it’s supposed to mean. Engineering owns the how; this role owns the what and the why.

Closes the loop on adoption. Supports validation and testing against real clinical scenarios rather than functional checks alone, and stays engaged until the people who asked for the data product are actually using it.

Sets the direction. Drives clinical data strategy by embedding CDISC standards, data domains, and governance into how data is built — the discipline that makes datasets trustworthy enough to be analytics- and AI-ready.

Mandatory — CDISC / SDTM / ADaM (applied)

• Solid working knowledge of CDISC
  , sufficient to design systems and pipelines that produce conform ant data.
• Demonstrated source-to-SDTM mapping
  : the ability to take raw,…