mural - Senior Product Manager, Data Products
Requirements
• 5+ years of product management experience, including shipping at least one analytics or reporting product used by paying business customers. You defined the metrics behind it, not just the UI. • A track record of making messy or conflicting data trustworthy: reconciling competing definitions, writing metric specs engineers could build from, and getting instrumentation changed upstream. • Experience building both platform products, where your customers are internal teams or developers, and end-user-facing data products like analytics, reporting, or insights. You understand how the two have to work together. • Working knowledge of modern data stacks: event pipelines, warehouses and lakehouses, dbt-style modeling, and semantic layers. You can write SQL and pull data yourself. • The ability to get agreement on definitions across teams you don't manage, including PMs, data engineering, analytics, finance, and go-to-market. • Experience with enterprise admin or governance products (IAM, security, compliance, audit) • Hands-on experience with semantic layer tools (dbt Semantic Layer, Cube, LookML) • Experience designing APIs, MCP servers, or connectors for analytics • EQUAL OPPORTUNITY • We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Responsibilities
• The evolution of data products. Shape how Mural's data products move from reporting on what happened to guiding what happens next. Work across the Govern & Trust portfolio to define how insights become part of how organizations run Mural. • The metric layer. Define Mural's shared, cross-product metrics, like activity, collaboration, and adoption. Set the standards that let product teams define and publish their own metrics consistently. Data Engineering builds and runs the layer; you shape what goes in it and how it evolves. • Internal product analytics. Be a thought partner to domain PMs as they set their north-star metrics, and make product data easy for them to explore on their own. Work with Business Analytics to keep product-facing and business-facing reporting working together. • Customer-facing data products. Own the strategy and roadmap for Insights: in-product reporting and dashboards, exports and APIs, audit logs, and the methodology behind every number customers see. • AI and agent access. Package and launch Mural's customer-facing reporting MCP server: which data it exposes, which plans get it, and what guardrails apply. Define how natural-language and agent-driven analysis extends Insights. • Instrumentation. R&D teams own emitting events. Your job is to raise the bar on what they emit. Set per-product requirements for granularity and retention, which differ for audit logs, activity data, and product analytics. Partner with Data Engineering on event standards so metrics start from good data.
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