Data Engineer, Expert

Requisition ID# 171886

Job Category: Information Technology

Job Level: Individual Contributor

Business Unit: Strategy & Growth

Work Type: Hybrid

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. This team of forward–thinking individuals is tasked with deploying the technology and infrastructure to achieve the company’s reliability goals. The team is responsible for implementing programs required to modernize the electric grid allowing for safe, resilient and efficient operations.

Position Summary

Designs, develops, modifies, configures, debugs and evaluates jobs for extracting data from various sources, implements transformation logic, and stores data in various formats fit for use by stakeholders. Collects metadata about jobs including data lineage and transformation logic. Works with teams, clients, data owners and leadership throughout the development cycle practicing continuous improvement.

This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the Bay Area of the PG&E Service Territory.

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, specific skills, education, licenses or certifications, experience, market value, geographic location, 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.

Bay Minimum: $140,000
Bay Maximum: $2238,000

This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.

Job Responsibilities

• Leads a team on moderately complex to complex data and analytics-centric problems having 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 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.
• Provides assistance to 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 catalogued.
• 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/provides guidance to less experienced colleagues.

Qualifications

Minimum:

• BA/BS in Computer Science, Management Information Systems or related field of study, or equivalent experience
• 7 years of experience with data engineering/ETL ecosystems such as Palantir Foundry, Spark, Informatica, SAP BODS, OBIEE
• Experience with multiple data engineering/ETL ecosystems
• Experience with machine learning algorithm deployment

Desired:

• Masters Degree in Computer Science or job-related discipline or equivalent experience
• Leadership experience, development teams
• Business Intelligence and data access tool expertise.
• Knowledge of software engineering principals such as unit testing, CI/CD, source control.