Vice President of Analytics, Muck Rack

• 8+ years of progressive analytics leadership experience, including managing both analytics engineers and business analysts at a B2B SaaS company
• Track record of building and scaling embedded or hub-and-spoke analytics models where analysts sit within business functions but report centrally
• Deep understanding of the modern data stack. You’ve worked with dbt, Snowflake (or equivalent), and at least one major BI tool. You have a point of view on semantic layers and how they change the analytics operating model
• Experience acting as a “product manager” for an analytics function, including mapping stakeholder workflows, proactively building data products, and making deliberate prioritization decisions about where the team spends its time
• You’ve managed the tension between ad hoc requests and long-term infrastructure investment, and you have a framework for how to balance them
• Demonstrated ability to partner with and influence C-level and VP-level stakeholders across Sales, Finance, Product, and Customer Success, with strong executive presence and business acumen
• Comfort with AI/LLM-enabled analytics. You don’t need to be an ML engineer, but you should have a clear and informed point of view on how AI changes the analytics function and the ambition to build toward it
• Strong opinions about BI tooling, data quality, and what “self-serve analytics” actually looks like in practice (not just a buzzword)
• You can translate a business question into a data model, and a data model into a business narrative. You’re as comfortable in a board meeting as you are reviewing a dbt pull request
• Alignment with Muck Rack’s core values: Customer Devotion, Resilience, Transparency, Ownership
• Proactively incorporate AI tools into day to day work to improve productively and accelerate delivery

Desirable

• We’re looking for a VP of Analytics to build and lead the team that powers how Muck Rack understands its business
• This is our first dedicated analytics leadership hire at this level, and it represents an opportunity to shape the function from the ground up, owning the data platform, the metric definitions, and the analytical infrastructure that drives decision-making across every department
• You’ll lead a team of analytics engineers and business analysts, with the analysts embedded directly in departments
• Your job is to make sure the entire company is working from the same numbers, that our operating cadences are powered by trustworthy and automated data, and that we are building toward an AI-native analytics experience where stakeholders can access insights on demand
• What makes this role distinctive is that it’s not just about building dashboards and answering data questions
• You’ll act as a product manager for the analytics function, deeply understanding the workflows, decisions, and pain points of each business team your analysts serve, and proactively building the data products and tooling that help those teams perform at a higher level
• You’ll balance the long tail of ad hoc requests against building the next layer of infrastructure, and you’ll own the prioritization framework that keeps the team focused on the highest-leverage work
• This role reports directly to the COO/CFO
• Own the data and metric definitions:
• Define and maintain the single source of truth for every business metric at Muck Rack: from ARR and net retention to pipeline conversion and feature adoption
• Own the analytics engineering function: a small, high-leverage team that maintains our dbt project and builds the semantic layer that powers both human and AI-driven analytics and decides how data is modeled, curated and exposed
• Set the AI analytics roadmap: architect the path from today’s dashboard-driven reporting toward a natural language, self-service model where stakeholders can query metrics directly
• Lead embedded business analysts as their “product manager”:
• Manage and build a team of embedded business analysts each reporting to you while sitting functionally within their business team
• Deeply understand the workflows of each function your team serves. Map out what a CSM does in a given day, week, and quarter. Understand what the best-performing reps do differently. Identify where data and tooling can systematize what great looks like and build it
• Treat each department as a “customer” of the analytics team: understand their use cases, anticipate their needs, and proactively build the data products and dashboards that help them perform, rather than waiting for requests
• Set quality standards and methodology across all analysts to ensure consistency in metric definitions, analytical rigor, and output quality — regardless of which function they’re embedded in
• Build and run the BA community of practice: weekly syncs, peer review, shared learnings, and professional development
• Manage the intake and prioritization of analytical work:
• Build and operate the prioritization framework that balances the long tail of ad hoc requests against investment in infrastructure, automation, and proactive insight-building
• Be the gatekeeper: protect the team’s capacity for high-leverage work (semantic layer, AI tooling, automated reporting) while ensuring urgent business questions still get fast answers
• Make the trade-offs visible stakeholders should understand what the team is working on, why, and what’s in the queue
• Drive the BI and AI analytics strategy:
• Evaluate and evolve our BI tooling strategy, with a focus on enabling faster, more flexible report creation without tools and dashboards proliferating unchecked
• Build the semantic layer as the foundation for consistent metric definitions across every consumption layer including dashboards, ad hoc analysis, and AI agents
• Deliver a natural language querying capability that lets executives and operators get quick answers to metric questions without submitting a request
• How success will be measured in this role:
• Every department leader says they have better, more reliable data support than they did 6 months ago
• The company operates from a single, trusted set of metric definitions — no more “my number says X but your number says Y”
• Key company input metric readings are automated and delivered on time every week, with minimal manual effort
• The semantic layer covers the top 30+ business metrics and is actively used by analysts and (over time) AI agents
• Ad hoc request turnaround time decreases as self-serve capabilities and proactive analytics increase
• The embedded BA model produces measurable improvements in the functions they serve — better pipeline forecasting, earlier churn identification, more efficient marketing spend
• The team ships AI analytics capabilities (natural language querying, automated variance commentary) that reduce manual reporting burden

• We’ll keep you informed at every step and let you know if anything changes along the way:
• Intro call with a member of our Talent Team
• A video interview with the CFO/COO
• A case exercise: given a sample business scenario and dataset, walk us through how you would structure the analytics support, prioritize requests, and build toward proactive insight delivery
• Panel interviews with members of the executive team and key cross-functional stakeholders