Conceptualizes and generates infrastructure that allows big data to be accessed and analyzed.
Collaborate with cross-functional teams—including data scientists, analysts, and business stakeholders—to understand data requirements and deliver high-quality, analytics-ready datasets.
Support the deployment of machine learning models by enabling feature pipelines, model input/output data flows, and integration with platforms like SageMaker or Foundry.
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.

Requirements

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.
Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Familiarity with business intelligence tools such as Power BI, Tableau, or Foundry for data visualization and reporting.
Knowledge of software engineering principles such as unit testing, CI/CD, and source control.
Experience working with geospatial data and tools such as ArcGIS.
Experience building data migration pipelines in Informatica
Experience with machine learning algorithm deployment.

Benefits

This job can also participate in PG&E’s discretionary incentive compensation programs

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringETLmachine learningdata migration pipelinesunit testingCI/CDsource controlgeospatial datainfrastructure-as-codedata analysis

Soft Skills

collaborationcommunicationmentoringproblem-solvingjudgmentcreativityplanning