A data technology firm in California is looking for a product owner or project manager to lead data initiatives across business and engineering teams. This role requires a solid understanding of data warehousing and schemas, particularly with the Snowflake platform. You will not only manage requirements but also engage deeply with engineers, ensuring alignment between business objectives and technical capabilities. Key experience in data products and ETL processes is essential for this position.
Qualifications
Experience leading data products or data warehousing initiatives.
Solid understanding of schemas, databases, and ETL processes.
Primary data warehouse platform experience in Snowflake.
Responsibilities
Role must interface with both business and engineering teams.
Expected to work directly with engineering teams on the enterprise data platform.
Skills
Data ecosystem leadership
Understanding of schemas and databases
Familiarity with ETL processes
Engagement with engineering teams
Knowledge of data warehouse platforms
Tools
Snowflake
Informatica
Databricks
BigQuery
Redshift
Job description
Qualifications:
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.