Chevron is accepting online applications for the position Product Owner, Analytics through 08/18/2026 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 IQ, Customer Data Platforms, etc.).
• Ensure that metric definitions are structured and governed in a way that enables agentic AI and generative AI tools to consume and interpret analytics accurately.
• Evaluate and pilot emerging analytics capabilities (e.g., Databricks Genie, Copilot for Dynamics 365, Power BI Copilot) with an eye toward business value and responsible AI use.

8. Stakeholder & Vendor Management
• Maintain productive relationships with key external partners, including Microsoft and Salesforce account teams, System Integrators, etc.
• Participate in third-party risk assessments and compliance reviews as needed for analytics platforms.

Required Qualifications:

• Bachelor's degree in Business, Data Analytics, Information Systems, or a related work experience.
• 5+ years of experience in a business analytics, data product management, or insights role within a large enterprise environment.
• Demonstrated ability to translate complex business requirements into clear data product specifications for technical teams.
• Strong understanding of data modeling, ETL concepts, and analytics platform architecture (hands-on development not required, but conceptual fluency is essential).
• Basic SQL proficiency, or demonstrated willingness and ability to learn SQL, to support data validation, troubleshooting, and root cause analysis.
• Experience troubleshooting data discrepancies across multiple systems, including the ability to distinguish between technical data issues, business logic gaps, and interpretation differences.
• Experience working with CRM platforms (Salesforce, Dynamics 365) and business intelligence tools (Power BI, Tableau, CRM Analytics).
• Excellent written and verbal communication skills, with the ability to present insights and recommendations to senior leadership.
• Proven ability to manage multiple stakeholders across different business areas simultaneously.
• Strong organizational skills with experience managing a prioritized backlog of analytics requests.
• Comfort operating in a matrixed environment where data engineering, platform development, and business ownership are distributed across teams.

Preferred Qualifications:

• Experience in downstream oil & gas, CPG, or B2B/B2C commercial operations.
• Familiarity with Salesforce CRM Analytics (formerly Einstein Analytics / Tableau CRM), including dataflows, recipes, and SAQL at a conceptual level.
• Experience with Microsoft Fabric, Databricks, or modern data lakehouse architectures.
• Exposure to AI/ML concepts, including semantic models, agentic AI, and generative AI applications for analytics.
• Salesforce or Microsoft certifications related to data and analytics (e.g., CRM Analytics Consultant, Power BI Data Analyst, Dynamics 365 Fundamentals).
• Prior experience supporting a platform migration where analytics continuity was a key workstream.
• Experience building data dictionaries, metric taxonomies, or semantic layers.

Relocation Options:

Relocation is not offered for this role. Only local candidates will be considered.

International Considerations:

Expatriate assignments will not be considered.

Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position