Principal Data Engineer, Biometrics

Neurocrine Biosciences•San Diego, CA

2d•$132,700 - $182,000

About The Position

Neurocrine Biosciences is hiring an experienced data engineering professional to design, develop, implement, and maintain complex data engineering solutions supporting Clinical Data Management, Statistical Programming, and Biostatistics. The role works across the regulated data path from clinical data collection through analysis datasets and regulatory submissions. It requires fluency in CDISC standards and industry-standard clinical data models, along with the software engineering discipline to build, validate, and maintain tools in a GxP environment. The position focuses on accelerating study database builds, improving data quality, making dataset and output generation more efficient, and helping Biometrics scale as the company’s pipeline grows. The engineer analyzes complex technical challenges, partners across functions to understand needs and recommend solutions, and translates business requirements into sustainable data engineering solutions. The role also contributes to technical standards and best practices and supports adoption through documentation, training, and ongoing support.

Requirements

Bachelor’s degree in Computer Science, Engineering, Bioinformatics, Statistics, or a related field and 6 or more years in data engineering, statistical programming, or clinical data management, including work in a regulated clinical environment; or a master’s degree in one of those fields and 4 or more years of such experience.
Advanced knowledge of CDISC standards, including CDASH, SDTM, ADaM, controlled terminology, and define.xml, and their application across clinical data collection, analysis, and regulatory submission.
Demonstrated experience designing, developing, and maintaining complex, production-grade data engineering solutions supporting clinical data management, statistical programming, or biostatistics.
Strong proficiency in Python, SQL, database design, and data modeling, with working knowledge of SAS and/or R.
Advanced knowledge of software engineering best practices, including Git/GitHub, CI/CD, automated testing, and maintainable code development.
Experience with clinical data structures and integration across EDC, eCOA, CTMS, and external vendor systems.
Experience applying GxP software validation (CSV/CSA), 21 CFR Part 11 requirements, and risk-based validation practices to regulated software solutions.
Strong understanding of FDA clinical study data submission expectations, including the Study Data Technical Conformance Guide and conformance tools such as Pinnacle 21.
Experience operationalizing AI/LLM workflows, including prompt and retrieval design, output evaluation, monitoring, and validation appropriate for regulated environments.
Working knowledge of statistical concepts and methods, including descriptive statistics, probability, and hypothesis testing.
Experience in the biopharmaceutical industry or a contract research organization supporting clinical trials.
Ability to analyze and resolve complex technical and business problems, applying sound judgment to develop effective solutions.
Strong analytical, critical-thinking, and problem-solving skills, including the ability to evaluate alternatives and cross-functional impacts.
Excellent communication, collaboration, and influencing skills, with the ability to work effectively with cross-functional stakeholders and support alignment and adoption.
Ability to provide technical guidance, contribute to best practices, and support less-experienced colleagues without direct supervisory authority.
Strong organizational and prioritization skills, with the ability to lead complex projects and initiatives, manage competing priorities, and deliver results aligned with functional objectives.
Ability to identify opportunities for continuous improvement and implement process improvements and scalable solutions that enhance operational effectiveness.

Nice To Haves

Experience with cloud computing platforms, preferably AWS.
Familiarity with Databricks and/or Spark.

Responsibilities

Collaborate with Clinical Data Management, Statistical Programming, and Biostatistics to evaluate workflows, identify improvement opportunities, define solution requirements, and establish measurable success criteria.
Translate Biometrics processes into maintained, production-grade data products with clear ownership, versioning, and support.
Design, build, and maintain pipelines that move clinical data from EDC, eCOA, central labs, and other vendors into standards-compliant, analysis-ready datasets.
Develop automation across the CDISC data flow, including CDASH-aligned collection, SDTM dataset generation, and support for ADaM and TFL production, with end-to-end traceability.
Build data quality and review tools, including automated checks and outlier detection, to support exception-based workflows.
Develop tools to 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 automation.
Support Medical Writing and Biostatistics with automated, standards-driven document outputs.
Help define vendor data transfer specifications and implement direct vendor-to-EDC and vendor-to-lake transfers with integration and platform colleagues.
Apply SDLC and computer system validation practices for GxP-related systems, including requirements, risk assessment, testing, traceability, change control, and release documentation.
Support implementation and adoption through user training, documentation, ongoing feedback, and measurement of usage, time saved, and quality improvements.
Collaborate with platform and AI engineering colleagues to align Biometrics data products with the enterprise data lake, governance model, and downstream analytics.
Advise Biometrics and Clinical stakeholders, evaluate emerging technologies and vendor solutions, and recommend solutions that meet business and technical requirements.
Perform other job-related duties as assigned.