New

Workforce Data Engineer

University of California - San Francisco

90,700-160,000

United States, California, San Francisco

654 Minnesota Street (Show on map)

Jul 08, 2026

Certain terms and conditions of employment for this position, including the rate of pay, benefits, etc., are currently subject to negotiation with the appropriate union.

As part of the People Analytics team, the Workforce Data Engineer develops governed data models that support workforce analytics and other priorities established by UCSF leadership and the Chief Human Resources Officer. The incumbent integrates data from multiple enterprise systems, develops dimensional data models, and maintains data pipelines that support reporting, dashboards, and AI-enabled analytics platforms. Using established Kimball modeling standards and engineering practices, the engineer creates reliable, reusable analytic datasets while ensuring data quality, consistency, and adherence to governance requirements.

The role supports a broad portfolio of People Analytics domains, including workforce demographics, workforce lifecycle metrics, human resources operational analytics, occupational health, learning and compliance, and other workforce-related initiatives.

Working closely with data architects, analysts, and business stakeholders, the incumbent contributes to analytic solution design, validates outputs, and translates workforce requirements into technical solutions. The position requires strong technical skills, sound judgment, and the ability to independently execute complex assignments while escalating novel architectural, governance, or enterprise-wide decisions to senior team members.

Department Overview

The mission of the Enterprise Information and Analytics (EIA) department is to advance UCSF's institutional priorities by enhancing access to and the use of data and analytics, including appropriate data platforms and data governance, in a manner that meets the unique needs across the education, research, and care mission areas. The department achieves its goals by working with clinical, research, education, and business partners to organize, integrate, govern, and transform UCSF's data assets into actionable insight and information.

People Analytics owns enterprise-wide assets for UCSF as a whole, used for both operational and analytical purposes, as well as departmental and business assets to drive business insights and decisions.

% of time	Essential Function (Yes/No)	Key Responsibilities (To be completed by Supervisor)
25		Business Requirements and Workforce Analytics Partnership Partner with workforce analysts, HR stakeholders, and business leaders to understand workforce reporting, analytics, and operational requirements. Collaborate with analysts to translate workforce metrics, business rules, and reporting requirements into technical specifications and sustainable data solutions. Evaluate source system data and identify required data elements to support workforce analytics initiatives. Support development, testing, and validation of new workforce metrics and analytic products. Contribute to analytic solution design by providing technical guidance on data structures, integration approaches, and implementation considerations. Document business and technical requirements, data definitions, assumptions, and transformation logic.
35		Data Engineering & Data Modeling Design, develop, maintain, and optimize dimensional data models supporting workforce analytics, operational reporting, and strategic decision-making. Build and maintain ETL processes, stored procedures, and SSIS pipelines that support workforce data integration and analytics. Leverage UCSF's established Kimball data architecture to develop governed and reusable workforce data assets. Integrate data originating from Oracle HCM Cloud, and other systems into analytic data models. Develop and maintain technical documentation for data models, pipelines, and data transformations. Collaborate with data architects and senior engineers to ensure solutions align with enterprise architecture, governance, and engineering standards.
25		Data Quality & Workforce Data Stewardship Investigate, troubleshoot, and resolve data quality issues affecting workforce reporting, dashboards, and downstream analytic products. Support efforts to standardize, reconcile, and improve workforce data across multiple enterprise systems and business processes. Validate analytic datasets, metrics, and reporting outputs to ensure accuracy, consistency, and alignment with business requirements. Implement data quality controls, monitoring processes, and validation procedures to support reliable analytics. Participate in testing, deployment, and ongoing maintenance of workforce analytics data assets. Contribute to the development of new workforce metrics and analytic capabilities as organizational priorities evolve.
15		Operations & Production Support Monitor, troubleshoot, and resolve issues affecting workforce data pipelines, models, dashboards, and reporting solutions. Perform break-fix support and root cause analysis for production data issues. Support deployment activities, system upgrades, and enhancements to workforce analytics platforms. Maintain technical documentation and operational procedures. Participate in continuous improvement efforts that improve reliability, maintainability, and scalability of People Analytics data products. Perform other duties as assigned.
100%		(To update total %, enter the amount of time in whole numbers (without the % symbol - e.g., 15, 20) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9. The total sum should add up to 100%.)

REQUIRED QUALIFICATIONS

Bachelor's degree in a related area and/or equivalent experience/training
Minimum 5 years of experience in data analytics or data management
Minimum 3 years of experience performing advanced data analysis using SQL and/or analytic programming languages (e.g., R, Python, Stata, SPSS) T-SQL in Microsoft SQL Management Studio preferred.
2+ years of experience developing or integrating with APIs for data ingestion (REST/JSON), including secure authentication (OAuth2/API keys) and operational support (logging/monitoring/error handling).
Experience writing SQL for data extraction, transformation, and analysis. Hands-on experience developing and supporting SSIS pipelines for analytic data preparation
Experience developing or maintaining ETL processes.
Experience integrating data from multiple source systems.
Experience validating data quality and troubleshooting data issues.
Ability to communicate effectively with both technical and non-technical audiences.
Strong analytical, problem-solving, and organizational skills.
Ability to work independently while collaborating effectively within a team.
Strong organizational skills

PREFERRED QUALIFICATIONS

Knowledge of Kimball dimensional modeling concepts and data warehousing principles.
Experience integrating data from diverse sources and formats, including delimited text files (CSV/TSV/PSV), fixed-width files, JSON, XML, YAML, Parquet/Avro/ORC, and Excel (XLS/XLSX), as well as relational databases via ODBC/JDBC.

REQUIRED QUALIFICATIONS

Bachelor's degree in a related area and/or equivalent experience/training
Minimum 5 years of experience in data analytics or data management
Minimum 3 years of experience performing advanced data analysis using SQL and/or analytic programming languages (e.g., R, Python, Stata, SPSS) T-SQL in Microsoft SQL Management Studio preferred.
2+ years of experience developing or integrating with APIs for data ingestion (REST/JSON), including secure authentication (OAuth2/API keys) and operational support (logging/monitoring/error handling).
Experience writing SQL for data extraction, transformation, and analysis. Hands-on experience developing and supporting SSIS pipelines for analytic data preparation
Experience developing or maintaining ETL processes.
Experience integrating data from multiple source systems.
Experience validating data quality and troubleshooting data issues.
Ability to communicate effectively with both technical and non-technical audiences.
Strong analytical, problem-solving, and organizational skills.
Ability to work independently while collaborating effectively within a team.
Strong organizational skills

PREFERRED QUALIFICATIONS

Knowledge of Kimball dimensional modeling concepts and data warehousing principles.
Experience integrating data from diverse sources and formats, including delimited text files (CSV/TSV/PSV), fixed-width files, JSON, XML, YAML, Parquet/Avro/ORC, and Excel (XLS/XLSX), as well as relational databases via ODBC/JDBC.