Data Engineering Leader

Core technical expectations

• Strong data ecosystem background: experience leading data products or data warehousing initiatives.
• Solid understanding of schemas, databases, and ETL (Extract, Transform, Load) processes; no hands-on coding expected but must be able to engage deeply with engineers and "know what they are talking about.”
• Primary data warehouse platform: Snowflake.
• Broader big data background acceptable if not purely Snowflake (e.g., Databricks, BigQuery, Redshift).
• Current ETL tool: Informatica; hands-on Informatica expertise is not required.
• Analytics/visualization not in scope; Power BI knowledge is a nice-to-have, not mandatory.

Role and scope

• Titles vary across industry: product owner (PO), project manager, scrum master, TPM; at PG&E, similar roles may be labeled "product managers.”
• Not seeking a pure scrum master or a typical external-facing PO who only writes requirements.
• Role must interface with both business and engineers; expected to work directly with engineering teams.
• Focus is on the enterprise data platform and data engineering; not an analytics/visualization role.

Core technical expectations

• Strong data ecosystem background: experience leading data products or data warehousing initiatives.
• Solid understanding of schemas, databases, and ETL (Extract, Transform, Load) processes; no hands-on coding expected but must be able to engage deeply with engineers and "know what they are talking about.”
• Primary data warehouse platform: Snowflake.
• Broader big data background acceptable if not purely Snowflake (e.g., Databricks, BigQuery, Redshift).
• Current ETL tool: Informatica; hands-on Informatica expertise is not required.
• Analytics/visualization not in scope; Power BI knowledge is a nice-to-have, not mandatory.