Data Engineering Product Manager - Oakland, CA (Hybrid)
Role summary
This hybrid role in Oakland, CA, is for a Data Engineering Product Manager focused on the enterprise data platform. The position requires a strong understanding of data ecosystems, including schemas, databases, and ETL processes, with a primary focus on Snowflake. While hands-on coding is not expected, deep engagement with engineering teams is crucial. The role involves leading data products and data warehousing initiatives, interfacing with both business stakeholders and engineers. Experience with broader big data platforms like Databricks, BigQuery, or Redshift is acceptable. This is not an analytics or visualization role.
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.
Role and scope
Titles vary across industry: product owner (PO), project manager, scrum master, TPM;
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.