Data & Insights

Role overview A remote-first documentation platform is hiring a senior data generalist to turn analytics into a core lever for product and business decisions. This is a highly autonomous, cross-functional role sitting at the intersection of data, product, and business, with the freedom to define what to build, what to buy, and how to evolve the data environment over time.

Responsibilities - Partner with product and engineering teams to identify opportunities, define metrics, run experiments, and influence the roadmap. - Translate ambiguous business questions into analyses, insights, and actions that move key product and company metrics. - Collaborate on long-term data architecture, foundations, and tooling decisions alongside senior data leadership. - Evolve data models, pipelines, instrumentation, and internal tools as the company's needs change. - Evaluate build-versus-buy options and choose pragmatic solutions for each data problem. - Enhance an existing internal data agent so it becomes more useful and accessible across the organization. - Enable other teams to work more autonomously and effectively with data.

Requirements - Strong SQL, data modeling, and analytics skills, with comfort moving between analysis, data foundations, and practical tooling. - A product mindset: genuine curiosity about users, behavior, and how work translates into measurable product outcomes. - Demonstrated ownership and judgment when faced with ambiguous problems, knowing when to go deep, move fast, or keep things simple. - Ability to collaborate across product, engineering, growth, marketing, and leadership, with clear communication and a willingness to challenge assumptions. - A bias for impact, focused on what actually changes because of the work rather than what simply ships. - Willingness to learn, evaluate, and evolve tools across a modern data stack that includes a cloud data warehouse, transformation layer, internal app tooling, BI and exploration platforms, and product-event instrumentation.

Nice to have - Hands-on experience shaping data foundations, not just consuming them. - Familiarity with experimentation design and interpreting product experiment results. - Experience introducing or scaling AI-assisted or agent-based internal data tools.