Data Engineer, Expert

Pacific Gas And Electric Company•Oakland, CA

8d•$140,000 - $238,000•Hybrid

About The Position

PG&E is seeking an Expert Data Engineer to lead development of a modern, cloud-native data platform supporting vegetation management operations, regulatory compliance, and advanced analytics. The role will transform fragmented legacy systems into a unified, audit-ready Snowflake Data Lakehouse, enabling scalable, secure, and transparent access to data across the organization. The engineer will design and implement ELT pipelines using Snowflake, Airflow, Informatica, and AWS services including Step Functions, Fargate, Lambda, DynamoDB, and S3. The work covers structured, semi-structured, and unstructured data, integrating sources such as Salesforce (OneVM), SAP, SharePoint, ArcGIS, and remote sensing imagery. The position is hybrid, working from a remote office and an assigned work location within the PG&E Service Territory, based on business need.

Requirements

Bachelor’s degree in computer science, Management Information Systems, or a related field, or equivalent experience.
At least 7 years of experience with data engineering/ETL ecosystems such as Palantir Foundry, Spark, Informatica, SAP BODS, or OBIEE.
Experience with multiple data engineering/ETL ecosystems.
Experience with machine learning algorithm deployment.

Nice To Haves

Master’s degree in Computer Science, Management Information Systems, or a related field, or equivalent experience.
Hands-on experience designing and building scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data.
Experience with cloud data platforms such as Snowflake, Databricks, or AWS Redshift.
Experience with data integration tools such as Informatica or AWS Glue.
Experience developing with distributed data processing frameworks such as Apache Spark.
Proficiency in SQL and at least one programming language such as Python, Scala, or Java.
Ability to collaborate across teams and communicate technical concepts to non-technical stakeholders.
Knowledge of unit testing and test-driven development, CI/CD, and source control tools such as Git and GitHub.
Experience with cloud services, preferably AWS, including S3, Lambda, Step Functions, Fargate, DynamoDB, SNS/SQS, and CloudWatch.
Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
Experience deploying and operationalizing machine learning models in production environments.
Familiarity with business intelligence tools such as Power BI, Tableau, or Foundry.
Experience working with geospatial data and tools such as ArcGIS.
Experience with data cataloging, metadata management, and data lineage tools such as Informatica and Collibra.

Responsibilities

Design, build, and maintain scalable data pipelines using Airflow, Informatica, AWS services, and Snowflake for ingesting, transforming, and curating structured, semi-structured, and unstructured data.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver analytics-ready datasets.
Implement and optimize data lakehouse architectures, supporting efficient data flow across Bronze, Silver, and Gold layers in Snowflake.
Support machine learning model deployment by enabling feature pipelines, model input/output data flows, and integration with platforms such as SageMaker or Foundry.
Apply software engineering practices including version control, CI/CD, unit testing, and infrastructure-as-code to ensure reliable, maintainable data workflows.
Monitor and troubleshoot data pipelines, ensuring data quality, lineage, and governance standards across environments.
Mentor junior engineers and contribute to architectural decisions, code reviews, and knowledge sharing.
Communicate technical concepts and project updates to technical and non-technical audiences.