Lead Marketing Analyst

Strava

Lead Marketing Analyst in charge of brand media measurement at Strava. Collaborating with cross-functional teams to derive actionable insights from data-driven analytics.

Posted 4/26/2026full-timeStrava NY • New York • 🇺🇸 United StatesSenior💰 $205,000 - $227,000 per yearWebsite

Tech Stack

Tools & technologies

ETLPythonSQLTableau

About the role

Key responsibilities & impact

Own brand media measurement planning and strategy
Lead the application, governance, and reporting cadence of the brand measurement framework
Drive alignment across teams by gathering requirements from partners
Deliver persuasive and data-driven media performance reviews
Provide the “source of truth” for internal consumers by owning critical analytical reporting
Apply expertise in brand health surveys
Create and manage data pipelines from multiple sources
Collaborate with Strava's broader data organization

Requirements

What you’ll need

5+ years of full-time experience in analytics, data science, or other quantitative domains supporting growth marketing teams
3+ years of experience with performance measurement across paid and organic channels, including TikTok, Meta, Reddit, and similar platforms
Track record of defining and building the key metrics and dimensions that accurately reflect user behavior and drive meaningful marketing decisions
Highly skilled in SQL, with extensive experience extracting large datasets and designing ETL workflows
Proficient in quantitative programming languages like Python
Experienced with Tableau
Deep technical expertise in brand measurement methodologies, including Marketing Mix Modeling (MMM)
High ownership of work and collaboration across the business
Comfortable operating in ambiguity and making informed recommendations

Benefits

Comp & perks

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Hard Skills & Tools

analyticsdata scienceperformance measurementSQLETL workflowsPythonTableaubrand measurement methodologiesMarketing Mix Modelingdata pipelines

Soft Skills

collaborationownershipcommunicationpersuasiondata-driven decision makingoperating in ambiguity