VP, Analytics

What you'll do:

• 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

If the details below describe you, you could be a great fit for this role:

• 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

Nice to haves:

• Background in or exposure to media, PR, communications, or content-oriented businesses
• Experience evaluating or leading a BI tool migration (e.g., from Looker to Hex, or from...)