Responsibilities

• Own the data model and metric definitions. Document and maintain a single, authoritative marketing data model - funnel stages, what an MQL actually is, how campaign attribution works. Every dashboard, every report, every executive readout is grounded on it. When two dashboards show seemingly different numbers for the same metric, your job is to diagnose and either educate the team on the interpretation, or fix the underlying data model fast.
• Deliver marketing measurement end to end. Marketing performance across the funnel, delivered as compelling stories grounded in the business, and that come with recommendations - not just numbers. You lead with the conclusion, calibrate the depth to your audience, and own the "so what."
• Work the business problem with stakeholders. Commercial marketing, product marketing and digital marketing leaders are your primary customers. You work alongside them on why a channel is underperforming, where the funnel leaks, which segments to lean into. You bring problem-statement discipline: they describe the symptom from where it affects them, you determine the root cause.
• Own lead quality measurement. MQL definition and lifecycle metrics, lead scoring validation, and the analysis that tells us whether marketing is delivering a pipeline that closes.
• Build two distinct delivery channels. Self-serve: publish clear, documented endpoints and datasets so leaders who want to query the data themselves can do it accurately. Full-serve: curated visuals, recurring digests, and narrative summaries for stakeholders who want trusted, actionable answers on a fixed cadence.
• Be the quality layer. Sanity-check self-served numbers before they reach a decision. Set up automated validation checks so interpretation or reporting problems surface fast, not after someone puts a bad number on a slide.
• Build AI-assisted analytics tools. Maintain Marketing’s semantic layer and metric dictionary to ensure both Tableau dashboards and AI-assisted self-serve interfaces deliver standardized answers
• Steward the marketing data product layer. Marketing owns a governed Snowflake database that feeds lead scoring, Data Cloud, and downstream reporting. You can expect extensive support from the BI team on the underlying data layers that feed this database and their interfaces to Marketing’s database, but ultimately you are its owner: you know what's in it, what depends on it, and what breaks if it changes. You work with our BI team as a knowledgeable customer of their platform, owning marketing's translation of it.
• Represent and advocate for marketing analytics requirements as the marketing technology stack evolves - Salesforce Marketing Cloud migration, Segment instrumentation, eCommerce replatforming, and whatever comes next.

Requirements

• 7-10+ years in marketing analytics, business analytics or a closely related field, including B2B funnel experience
• Demonstrated ability to move from data to insight to recommendation - you can point to specific business decisions that changed because of your analysis
• Strong storytelling and communication. You lead with the conclusion, calibrate to your audience, and are comfortable presenting to senior leaders. You are particularly strong at clearly communicating complex topics using data visualizations that can intuitively and quickly tell a story to busy leaders, while also being faithful representations of the underlying data. You know that a well-framed insight can often have more of a business impact than a dense dashboard.
• Hands-on fluency with AI tools (Claude, Gemini or similar) for real analytical work - using natural language to get from data to answer, and knowing how to validate what comes back. We believe LLMs are starting to collapse the layer between a question and a dataset; we want someone who sees that shift as an advantage, not a threat
• Working proficiency in SQL and Tableau; comfort in a cloud data warehouse (Snowflake preferred).
• Practical fluency with marketing measurement concepts - attribution, campaign tracking, funnel and lifecycle metrics, lead scoring
• Collaborative by default: you surface overlaps and ambiguity early rather than working around them, and you'd rather make your stakeholders self-sufficient than indispensable to them

Preferred

• Life sciences, biotech or scientific-tools experience
• E-commerce analytics, especially mixed B2B and self-serve funnels
• Salesforce ecosystem experience - Salesforce Marketing Cloud especially; Marketo useful given our migration
• Experience building or maintaining semantic layers, metric catalogs, data dictionaries or self-serve analytics enablement
• Exposure to predictive modeling (churn, propensity, lead scoring) - you don't need to be a data scientist, but you should know when a model is the right answer and how to read one
• Experience working as a customer of a central BI or data platform team - you know how to partner effectively without duplicating their work