Golden State
Data Engineer
San Francisco, CA · Full-time
Sponsorship not specifiedDetected 1 day ago
PythonGitSQLAWSGCPAirflowdbtData EngineeringData ScienceCommunication
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16Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- About the Position The Golden State Warriors are looking for a Data Engineer to join our growing team.
- In this role, you will have large influence over our data pipelines and warehouse while collaborating with data science and analysts.
- This role is critical to our daily data operations, requiring a responsive approach to our data operations during the NBA season.
Responsibilities
- Developing batch and streaming / event-driven data pipelines
- Implement data validation and quality checks across pipeline stages
- Manage data pipelines during in-season operations
- Excellent written and verbal communication; strong presentation skills with the ability to influence technical and non-technical partners
- Ability to build positive working relationships with business partners and peers at all levels within the organization
Requirements
- Required Experience and Skills
Nice to have
- Data modeling and transformation for analytics and reporting
- Deploy predictive models into production
- Other duties as assigned
- 4+ years of data engineering or similar experience
- 3+ years of data operations or similar on-call experience
- Strong Python and SQL programmer
- Experience writing scalable ETL/ELT
- Experience with orchestration software like Airflow or Dagster
Compensation
- $165,000 Per Year + Bonus
Benefits
- Comprehensive Medical, Dental and Vision benefits for employees and dependents
- Vacation, Summer Half-Day Fridays and a generous paid time off plan for pregnancy and parental leaves
Company info
- Want to learn more about who we are and what we value?
Equal opportunity
- Golden State is an equal opportunity employer.
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This listing is sourced directly from Golden State's careers page and normalized into a canonical job model.