Senior Manager, Media Analytics

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Job Category

Marketing & Communications

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

About the Role

Salesforce’s Media Lab is looking for an exceptionally versatile and experienced Analytics Engineer to join the Media Intelligence team. This role is highly cross-functional, combining deep technical skills, business analytics, paid media domain expertise, and technical product management.
You’ll own the development of data assets, reporting systems, insights delivery, and stakeholder enablement for Salesforce’s global paid media operations. We are looking for someone who thrives in ambiguity, loves building, communicates with precision, and has the range to pivot between hands-on data engineering and high-level strategic storytelling.
If you're someone who can write production-grade SQL and also explain the business impact of MQL-to-pipeline conversion trends to a VP of Marketing — we want to meet you.

What You’ll Do

Engineering & Data Modeling

• Design, build, and manage large-scale datasets using SQL and dbt (Data Build Tool) to power dashboards, models, and self-serve analytics tools.

• Apply scrum-based engineering practices: peer code reviews, version control, technical documentation, and test coverage.

• Proactively identify and resolve data issues; troubleshoot joins, missing fields, or aggregation logic.

• Collaborate with data engineering teams to ensure data is modeled to serve both technical and business needs.

Dashboards & Reporting

• Build high-quality, visually intuitive dashboards and reports in Tableau.

• Develop business-ready reporting tools for use across regions, campaigns, vendors, and tactics.

• Support key business rhythms such as monthly/quarterly business reviews with insights and data deliverables.

• Continuously improve data assets for better performance diagnostics, forecasting, and goal tracking.

Analytics & Insights

• Extract insights from media and pipeline performance data across all stages of the B2B funnel.

• Conduct deep-dive analyses on campaign performance and spend efficiency.

• Identify key trends in spend, conversions, and ROAS across vendors, channels, and campaigns.

• Translate findings into clear, digestible, and actionable recommendations for marketing and media stakeholders.

Stakeholder Communication & Enablement

• Serve as a strategic liaison between Media Lab and our engineering and analytics partners.

• Deliver findings through live presentations, stakeholder briefings, and written narratives.

• Produce self-serve documentation: data dictionaries, metric definitions, and schema diagrams.

• Create short-form video tutorials and walkthroughs to help stakeholders navigate tools and dashboards.

• Lead live training and enablement sessions to empower teams with the knowledge to self-serve insights.

Technical Product Management

• Build and maintain project trackers, stakeholder update decks, and roadmaps for data-related initiatives.

• Partner with business and engineering teams to ensure alignment across data and reporting projects.

• Own stakeholder intake processes and help prioritize backlog items based on business impact.

• Ensure that the datasets and dashboards we deliver meet real user needs with clear, measurable value.

Who You Are

Qualifications

• 5+ years of experience in analytics or analytics engineering.

• 3+ years working with digital marketing data, preferably in a B2B SaaS context.

• Advanced expertise in SQL, dbt, and Tableau — including performance optimization and scalability.

• Proven success working with very large datasets and production-grade data pipelines.

• Demonstrated ability to crea