Principal Data Engineer, Biometrics

Data Engineering ProfessionalThis role will serve as an experienced data engineering professional responsible for designing, developing, implementing, and maintaining complex data engineering solutions that support Neurocrine's Clinical programs, including Clinical Data Management, Statistical Programming, and Biostatistics functions. This role sits on the regulated data path that runs from clinical data collection through analysis datasets, and ultimately submission to regulatory agencies. The ideal candidate brings fluency in CDISC standards (CDASH, SDTM, ADaM, and controlled terminology) and other industry-standard clinical data models, along with the software engineering discipline needed to build, validate, and maintain tools in a GxP environment. Work directly targets faster study database builds, higher data quality, more efficient dataset and output generation, and the ability of Biometrics to scale as Neurocrine's pipeline grows. This role analyzes and resolves complex technical challenges, partnering across functions to evaluate underlying needs, recommend appropriate solutions, and implement scalable improvements. The position requires sound judgment and the ability to translate business requirements into effective, sustainable data engineering solutions.

The role contributes to the development and implementation of technical standards and best practices, supports the adoption of solutions, and ensures appropriate documentation, training, and ongoing support to sustain long-term effectiveness.Your Contributions (include, but are not limited to):Collaborate cross-functionally with clinical Data Management, Statistical Programming, and Biostatistics to evaluate complex workflows, identify opportunities for improvement, define solution requirements, and identify measurable success criteria for data engineering initiativesTranslate Biometrics processes into maintained, production-grade data products with clear ownership, versioning, and supportDesign, build, and maintain pipelines that move clinical data from collection systems (EDC, eCOA, central labs, and other vendors) into standards-compliant, analysis-ready datasetsDevelop automation across the CDISC data flow, including CDASH-aligned collection, SDTM dataset generation, and support for ADaM and TFL production, maintaining traceability end to endBuild data quality and review tools, such as automated checks and outlier detection, that shift review from manual inspection to exception-based workflowsDevelop tools that accelerate study start-up, including protocol-to-SoA-to-CRF review, standards-based EDC database build, and protocol-to-SAP and SAP-to-TFL shell automationSupport Medical Writing and Biostatistics with automated, standards-driven document outputsAssist in defining vendor data transfer specifications and implementing direct vendor-to-EDC and vendor-to-lake transfers in partnership with integration and platform colleaguesApply established SDLC and computer system validation practices for GxP-related systems, including requirements, risk assessment, testing, traceability, change control, and release documentation, to ensure compliant, inspection-ready solutionsSupport successful implementation and adoption of delivered tools through user training, documentation, and ongoing feedback, and measure impact through usage, time saved, and quality improvementsCollaborate with platform and AI engineering colleagues so Biometrics data products align with the enterprise data lake, governance model, and downstream analyticsServe as a technical resource and advisor to Biometrics and Clinical stakeholders, providing technical recommendations, evaluating emerging technologies and vendor solutions, and recommending solutions that address business and technical requirementsOther job-related duties as assignedRequirements:Bachelor's Degree in Computer Science, Engineering, Bioinformatics, Statistics, or a related field AND 6+ years in data engineering, statistical programming, or clinical data management, including work in a regulated clinical environment ORMaster's Degree in Computer Science, Engineering, Bioinformatics, Statistics, or a related field AND 4+ years as noted aboveAdvanced knowledge in CDISC standards, including CDASH, SDTM, ADaM, controlled terminology, and define.xml, with an understanding of their application across clinical data collection, analysis, and regulatory submissionDemonstrated experience designing, developing, and maintaining complex, production-grade data engineering solutions supporting clinical data management, statistical programming, or biostatisticsStrong proficiency in Python, SQL, database design, and data modeling, with working knowledge of SAS and/or RAdvanced knowledge of software engineering best practices, including Git/GitHub, CI/CD, automated testing, and maintainable code developmentExperience with clinical data structures and integration across EDC, eCOA, CTMS, and external vendor systemsExperience with cloud computing platforms, preferably AWS; familiarity with Databricks and/or Spark is a plusExperience applying GxP software validation (CSV/CSA), 21 CFR Part 11 requirements, and risk-based validation practices to regulated software solutionsStrong understanding of FDA clinical study data submission expectations, including the Study Data Technical Conformance Guide and conformance tools such as Pinnacle 21Experience operationalizing AI/LLM workflows, including prompt and retrieval design, output evaluation, monitoring, and validation appropriate for regulated environmentsWorking knowledge of statistical concepts and methods, including descriptive statistics, probability, and hypothesis testingExperience in the biopharmaceutical industry or a contract research organization (CRO) supporting clinical trialsDemonstrated ability to analyze and resolve complex technical and business problems, applying sound judgment to develop innovative and effective solutionsStrong analytical, critical thinking, and problem-solving skills, with the ability to evaluate alternative solutions and consider cross-functional impactsExcellent communication, collaboration, and influencing skills, with demonstrated ability to collaborate effectively with cross-functional stakeholders to support alignment and adoption of solutionsDemonstrated ability to provide technical guidance, contribute to best practices, and support the development of less-experienced colleagues without direct supervisory authorityStrong organizational and prioritization skills, with the ability to lead complex projects and initiatives, managing competing priorities and delivering results aligned with functional objectivesAbility to identify opportunities for continuous improvement, recommend and implement process improvements and scalable solutions that enhance operational effectiveness