Product Owner, Analytics

About this role

Chevron is accepting online applications for the position  Product Owner, Analytics  through 08/1 8/2026 at  11:59 p.m.   Central Time Zone   

The Analytics Product Owner owns the business logic, metric definitions, and data product requirements for analytics solutions across Finished Lubricants, Chevron Texaco Rewards (CTR) Loyalty, Fuels, Marine Lubricants, and Customer Service. An ideal candidate is able to deeply understand the business questions being asked, translate those into clear specifications for technical teams, and validate that delivered solutions accurately serve stakeholder needs.

As Chevron's downstream commercial organization migrates from Salesforce to Microsoft Dynamics 365, the analytics landscape that has been built over the past decade within Salesforce CRM Analytics (CRMA) must be reimagined for a new platform era. This role serves as the critical bridge between business stakeholders and the Data & Insights (DNI) engineering team — a "data translator" who ensures that the right data, with the right business context, reaches the right people at the right time.

Key Responsibilities:

1. Metric Ownership & Data Governance ·         Own and maintain the authoritative technical definition for key business metrics across all supported areas (e.g., CTR penetration, loyalty share, gap to goal, sell-in/sell-out volumes, enterprise value, MOS plan vs. actuals). ·         Establish and maintain a living data dictionary that maps business terms to their source systems, transformation logic, and intended interpretation. ·         Ensure metric definitions are consistent across platforms as capabilities migrate from CRMA to Power BI, Dynamics 365, and Databricks. ·         Partner with DNI Data Product team to enforce quality standards and resolve data discrepancies.

2. Requirements Translation & Data Product Specification ·         Serve as the primary liaison between business stakeholders (Sales, Marketing, Customer Service) and DNI engineering teams. ·         Translate business questions and reporting needs into clear, actionable data product requirements — including data sources, join logic, refresh cadence, access controls, and output specifications. ·         Prioritize and manage a backlog of analytics requests, balancing business urgency with engineering capacity. ·         Review and validate delivered data products and dashboards to confirm they accurately represent the intended business logic before release to stakeholders.

3. CRM Migration Analytics Continuity ·         Serve as the analytics subject matter expert during the Salesforce-to-Dynamics 365 CRM migration, ensuring that existing CRMA reporting capabilities are accurately documented and accounted for in the migration scope. ·         Work closely with RSM (system implementer), Microsoft, DNI, and business teams to validate that replacement analytics solutions in the new platform meet or exceed current capabilities. ·         Advise RSM and internal teams on how data links across Salesforce, Dynamics 365, Power BI, Databricks, Azure Data Lake, loyalty platforms, and other source systems to ensure reporting continuity and accurate integration design. ·         Maintain a migration risk register for analytics — identifying dashboards, data products, and integrations at risk of being lost, degraded, or de-scoped. ·         Coordinate with the KT documentation and recordings from the CRMA knowledge transfer sessions to ensure institutional knowledge is preserved and accessible.

4. Data Quality, Reconciliation & Analytical Debugging ·         Investigate data discrepancies by combining SQL-based validation, source-to-target reconciliation, and business context to determine whether issues are caused by source data, transformation logic, dashboard configuration, metric definition, or stakeholder interpretation. ·         Own the business-facing reliability of analytics data flows by understanding upstream and downstream dependencies, monitoring data readiness, and coordinating issue resolution across business, platform, and DNI engineering teams. ·         Translate technical outputs into business meaning, reconciling differences between system-calculated results, governed metric definitions, and stakeholder expectations. ·         Clearly communicate root cause findings, metric interpretation, data limitations, and recommended actions to stakeholders in a way that supports decision-making and builds trust in analytics outputs.

5. Insights Enablement & Business Storytelling ·         Develop and maintain standard business narratives, talking points, and interpretation guides for key analytics outputs (e.g., CTR program performance, sales dashboards, etc.). ·         Proactively surface insights and recommendations to business leaders — moving beyond reactive reporting to anticipatory analytics. ·         Build and deliver training materials that empower business users to self-serve on governed datasets and tools, reducing dependency on a single point of expertise. ·         Act as a "translation layer" during leadership reviews and cross-functional meetings, helping non-technical stakeholders understand what the data is (and isn't) telling them.

6. Self-Service Analytics & Adoption ·         Champion adoption of self-service analytics tools (Power BI, Dynamics 365 dashboards, new analytics agents) across business teams. ·         Define and promote best practices for dashboard design, data interpretation, and report distribution. ·         Establish a tiered support model: self-service (Tier 0), business team triage (Tier 1), and DNI engineering escalation (Tier 2) ·         Monitor usage analytics to identify under-utilized or redundant dashboards and recommend consolidation.

7. AI & Semantic Readiness ·         Partner with DNI and enterprise architecture teams to encode business-defined metrics into semantic models that support AI-ready data strategies (e.g., Databricks Unity Catalog, Microsoft Fabric

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