Hybrid (Oakland, CA) Principal role with a focus on reliability data engineering and analytics that directly supports PG&E’s electric reliability and resilience strategy. This position combines enterprise data governance, scalable architecture, and cross-functional leadership to help ensure reliability data is auditable, traceable, and ready for operational, regulatory, and long-term planning use.
What you’ll be doing
As a recognized industry expert, you will lead the design, governance, and evolution of enterprise data capabilities for reliability and resilience. You will define the technical vision and establish practices for developing, integrating, and managing reliability data and pipelines with an emphasis on data integrity, auditability, and readiness.
You will architect and deliver advanced, scalable data solutions that integrate complex multi-source datasets to support reliability performance assessment, risk modeling, and long-term grid planning. You will also help drive innovation in data engineering approaches to improve transparency and traceability, supporting decision-quality across the organization.
- Lead teams tackling complex data and analytics problems with significant impact, requiring in-depth analysis and judgment.
- Provide thought leadership on infrastructure for accessing and analyzing big data with verified data quality and properly captured, cataloged metadata.
- Partner with business and other departments to develop requirements and apply them to complex cross-functional systems.
- Support programmers and analysts on unusual or complex problems spanning multiple functional and technology areas.
- Collaborate across teams to meet cross-team needs and requirements.
- Drive departmental standards, norms, and new goals/objectives.
- Communicate recommendations orally and in writing.
- Mentor and guide less experienced colleagues.
Reliability data standards and governance
You will establish and enforce rigorous standards for data transformation, metadata management, and data lineage. This includes aligning reliability data pipelines with enterprise data governance frameworks and ensuring compliance with regulatory requirements.
Required qualifications
- BA/BS in Computer Science, Management Information Systems, related field of study, or equivalent experience.
- 10 years of experience with multiple data engineering/ETL ecosystems, including at least some of Palantir Foundry, Spark, Informatica, SAP BODS, and OBIEE.
- Proven track record implementing data warehouses and data lakes over the previous five years.
- Experience leading development teams.
- 3 years of experience in machine learning algorithm deployment and machine learning operations.
- Master’s degree in Computer Science, Management Information Systems, or related field, or equivalent experience.
Skills and tools
- Business intelligence and data access tools
- Software engineering principles including unit testing, CI/CD, and source control
- Data engineering and analytics including Palantir Foundry, Spark, Informatica, SAP BODS, OBIEE, data warehouses, data lakes
- Metadata management, data lineage, and data transformation
- Big data and machine learning algorithm deployment and machine learning operations
Compensation and work model
- Salary range (Bay Area): $155,000 (minimum) to $265,000 (maximum). Mid-point: $210,000.
- Work model: Hybrid, requiring reporting to the Oakland General Office at least 2 to 3 days per week; remaining days may be worked remotely depending on business needs.
Benefits
- Discretionary incentive compensation programs
Department overview
The System Performance, Reliability and Resiliency Strategy team within the Electric Transmission and Distribution Engineering organization plans, organizes, and manages resources to execute PG&E’s Electric Reliability Strategy and initiatives. Within this organization, the Reliability Data team develops and curates reliability data and data pipelines that meet auditable standards.
