Expert, Data Engineer - Reliability Data
Role details
Job location
Tech stack
Job description
Serves as a recognized subject matter expert in the design, development, and governance of reliability data and data pipelines supporting enterprise reliability and resilience strategy. Leads the architecture and delivery of scalable data solutions that extract, transform, and integrate complex data from diverse sources into high-quality, auditable datasets. Ensures data is accurate, consistent, and structured to support critical analysis, regulatory requirements, and strategic decision-making.
Establishes and enforces standards for data engineering, transformation logic, and metadata management, including comprehensive data lineage and audit documentation. Drives continuous improvement in data quality, pipeline performance, and governance practices to meet evolving regulatory and business needs. Provides technical leadership across the data lifecycle, guiding teams in the implementation of best practices and resolving complex data challenges.
Partners closely with cross-functional teams, data owners, and senior leadership to ensure reliability data solutions align with enterprise objectives and PG&E's Electric Reliability Strategy. Delivers expert-level insight into data integrity, risk, and compliance, enabling trusted, transparent decision-making. Represents the organization in internal and external forums, contributing to industry best practices in reliability data management and analytics.
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., Leads a team on moderately complex to complex data and analytics-centric problems having a broad impact that require in-depth analysis and judgment to obtain results or solutions. May contribute to the resolution of uniquely complex data and analytics-centric problems having a significant impact Identifies, designs, and implements internal process improvements, including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes. Resolves application programming analysis problems of broad scope within procedural guidelines. Assists other programmers/analysts on unusual or especially complex problems that cross multiple functional/technology areas. Conceptualizes and generates infrastructure that allows big data to be accessed and analyzed with verified data quality, and metadata is appropriately captured and cataloged. Collaborates with peers to develop departmental standards, norms, and new goals/objectives. Plans work to meet assigned general objectives; reviews progress regularly, and solutions may provide an opportunity for creative/non-standard approaches. Assesses data pipeline performance and suggests/implements changes as required. Communicates (oral and written) recommendations. Mentors/guides less experienced colleagues., * Delivering for our hometowns
- Serving our planet
- Leading with love
Our Virtues capture "who" we need to be:
- Trustworthy
- Empathetic
- Curious
- Tenacious
- Nimble
- Owners
Our Stands are "what" we will achieve together:
- Everyone and everything is always safe
- Catastrophic wildfires shall stop
- It is enjoyable to work with and for PG&E
- Clean and resilient energy for all
- Our work shall create prosperity for all customers and investors
Requirements
BA/BS in Computer Science, Management Information Systems, related field of study, or equivalent experience. 7 years of experience with data engineering/ETL ecosystem, such as Palantir Foundry, Spark, Informatica, SAP BODS, OBIEE. Experience with multiple data engineering/ETL ecosystems. Experience with machine learning algorithm deployment.
Desired: Master's degree in Computer Science, Management Information Systems, or related field, or equivalent experience. Experience leading development teams.
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.