Job Description
Job Location:
Oakland Department Overview The System Performance, Reliability and Resiliency Strategy team within the overall Electric Transmission and Distribution Engineering organization is responsible for planning, organizing, and managing the resources necessary to successfully execute PG&E's Electric Reliability Strategy and initiatives. Within this department the Reliability Data team is on point for a key role is developing and curating all reliability data and data pipelines so that they meet auditable standards. Position Summary Designs, develops, and leads the implementation of data pipelines and processes to extract, transform, and deliver high-quality reliability data from diverse and complex sources. Defines and applies transformation logic to ensure data is accurate, consistent, and structured to meet enterprise stakeholder needs. Establishes and maintains comprehensive metadata, including data lineage, transformation logic, and audit documentation, to support transparency, governance, and regulatory compliance. Supports the System Performance, Reliability and Resiliency Strategy team by ensuring the availability of accurate, auditable, and actionable data used to inform PG&E's Electric Reliability Strategy and initiatives. Drives improvements in data quality, pipeline performance, and governance practices to meet evolving regulatory and business requirements. Leads collaboration with cross-functional teams, data owners, and leadership to resolve complex data challenges, enhance data processes, and align solutions with enterprise objectives. Provides guidance to junior team members and contributes to the development of best practices and continuous improvement across the data lifecycle. This position follows a hybrid work model, requiring employees to report to their assigned office location at least two or three days per week. The remaining days may be worked remotely, depending on business needs. The headquarters is located in the Oakland General Office. PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, particular skills, education, licenses or certifications, experience, market value, geographic location, collective bargaining agreements, and internal equity. Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors. This job can also participate in PG&E's discretionary incentive compensation programs. A reasonable salary range is:
Bay Area Minimum:
$122,000 Bay Area Mid-Point:
$158,000 Bay Area Maximum:
$194,000 Job Responsibilities Conceptualizes and generates infrastructure that allows big data to be accessed and analyzed. Partners with various departments to understand and incorporate standards information and requirements into work procedures. Deploys machine learning algorithms in production environments. Resolves application programming analysis problems of moderate to complex scope within procedural guidelines. May seek assistance from the supervisor or more skilled programmers/analysts on unusual or especially complex issues that cross multiple functional/technology areas. Works on complex data and analytics-centric problems having a moderate impact that require in-depth analysis and judgment to obtain results or solutions Plans work to meet assigned general objectives; progress is reviewed upon completion, and solutions may provide an opportunity for creative/non-standard approaches. Communicates (oral and written) recommendations. Mentors/guides less experienced colleagues. Qualifications Minimum:
BA/BS in Computer Science, Management Information Systems, related field of study, or equivalent experience. 5 years of experience with data engineering/ETL ecosystem, such as Palantir Foundry, Spark, Informatica, SAP BODS, OBIEE.
Desired:
Experience with machine learning algorithm deployment. Knowledge, Skills, Abilities, and Competencies:
Business Intelligence and data access tool knowledge. Knowledge of software engineering principles such as unit testing, CI/CD, and source control.
