below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$155,337–$216,775
— the blue band above.
At the midpoint, this role pays about
$157k
versus about
$186k
for comparable roles.
Based on 240 similar postings.
Employer
About Neurocrine
Neurocrine Biosciences is a biopharmaceutical company focused on discovering and developing treatments for neurological and endocrine-related conditions, including tardive dyskinesia, Parkinson''s disease, and endometriosis. Industry: Biopharmaceuticals & Neuroscience
Neurocrine currently has
8 open roles
on FindRole.
The Principal Data Engineer, Biometrics joins the Biometrics team to design, develop, and maintain complex data engineering solutions supporting clinical programs, including Clinical Data Management, Statistical Programming, and Biostatistics. This role sits on the regulated data path from collection through regulatory submission. You will build production-grade pipelines moving clinical data from EDC, eCOA, and central labs into standards-compliant datasets. Key responsibilities include automating CDISC data flows, developing data quality tools, and implementing GxP-compliant software validation. Required skills include proficiency in Python, SQL, database design, and data modeling, with working knowledge of SAS and/or R. You must possess advanced knowledge of CDISC standards like SDTM and ADaM. The role solves technical challenges to accelerate study start-up and ensure scalable, high-quality data outputs for clinical trials.
What does a Data Engineer earn in California?
Median $187700 from 41 postings across 21 companies.
Design, develop, and maintain production-grade data engineering solutions for clinical data management and biostatistics.
Build pipelines to move clinical data from collection systems into standards-compliant, analysis-ready datasets.
Automate CDISC data flows including CDASH-aligned collection, SDTM dataset generation, and ADaM/TFL production.
Develop automated data quality tools for outlier detection and exception-based review workflows.
Create tools to accelerate study start-up, including EDC database builds and protocol-to-SAP automation.
Apply GxP software validation practices to ensure all regulated systems are compliant and inspection-ready.
Translate business requirements into scalable data products with clear ownership, versioning, and documentation.
Provide technical advice to stakeholders on emerging technologies and vendor solutions for clinical data.
What we're looking for
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.
Master's Degree in Computer Science, Engineering, Bioinformatics, Statistics, or a related field AND 4+ years in data engineering, statistical programming, or clinical data management.
Advanced knowledge in CDISC standards including CDASH, SDTM, ADaM, controlled terminology, and define.xml.
Demonstrated experience designing, developing, and maintaining complex, production-grade data engineering solutions for 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 applying GxP software validation (CSV/CSA), 21 CFR Part 11 requirements, and risk-based validation practices to regulated software solutions.
Experience in the biopharmaceutical industry or a contract research organization (CRO) supporting clinical trials.