Skip to content
IT Principal Data Engineer – Data Solutions
| Company | Neurocrine Biosciences |
|---|
| Location | San Diego, CA, USA |
|---|
| Salary | $130100 – $188550 |
|---|
| Type | Full-Time |
|---|
| Degrees | Bachelor’s, Master’s |
|---|
| Experience Level | Senior, Expert or higher |
|---|
Requirements
- Bachelor’s Degree in Computer Science, Information Technology or a related field and 6+ years in Data/Software Engineering role with relevant exposure to data ecosystems
- Master’s Degree in Computer Science, Information Technology or a related field and 4+ years as noted above
- Expertise in Python or other modern programming language development experience
- Knowledge of advanced level capabilities in SQL; relational and analytical database experience
- Solid working knowledge of ETL/ELT, Data Modeling, Data Quality, Data Visualization / Dashboarding
- Solid working knowledge of software development practices: agile/lean methodologies, version control systems (VCS), code reviews, CI/CD
- Experience with cloud-based data platforms (e.g. Databricks, Snowflake)
- Experience with cloud-based infrastructure (e.g. AWS) and related services (e.g. S3, Lambda, Redshift, Athena)
- Experience with data and pipeline performance tuning and optimization
- Experience independently managing projects
- Experience communicating technical concepts to non-technical teams
- Expert in Databricks on AWS
- Experience with technologies that increase data ingestion and curation speed, such as Fivetran and dbt
- Experience with dimensional modeling (e.g. Kimball methodology)
- Experience with Power BI Datasets and DAX language
- Experience in industry – Pharmaceuticals or Biotech; working with Commercial / Sales
- Working knowledge of sociotechnical systems needed for effective data governance – e.g. domain ownership, metadata management, use of Attribute-based access controls (ABAC)
- Working knowledge of design patterns to support data privacy policies – e.g. tokenization, masking, etc.
- Experience with Terraform
Responsibilities
- Acquiring/ingesting large varieties of structured and semi-structured data
- Architecting & modeling data
- Transforming data
- Ensuring data quality, governance and enablement
- Developing at scale and improving efficiency
- Leading projects
- Collaborating with cross-functional teams to understand business requirements
- Optimizing data and pipelines for performance, cost-efficiency, and scalability
- Mentoring others in data engineering and data modeling
- Measuring data utilization and quality to surface progress and improvement opportunities
- Lead business enablement sessions, code reviews & documentation
- Leading adoption of technologies and patterns that enhance data engineering capabilities
- Leading adoption of technologies and patterns that enable distributed data governance and adherence to policies and regulations
Preferred Qualifications
No preferred qualifications provided.
Benefits
No information provided on Benefits.
