The opportunity
To understand the role, it helps to understand how our team works today.
What you'll do
Can you identify and hire talented people? We will continue to grow the: analyst team, and you will play a meaningful role in finding and assessing future teammates. Great analysts combine technical judgment, curiosity, communication, business context, and a willingness to own work end to end. Can you recognize that combination in others?
Can you develop and amplify the data practices that let a small team do: excellent work? Ashby’s Data team is lean, and our practices will need to evolve as the company becomes more complex. I see this role as an opportunity to improve the ways we prioritize work, review it, share knowledge, maintain quality, and collaborate.
Can you lead the team through the transition from broad shared context to: meaningful specialization? Today, each analyst can know a great deal about the company and data stack. That will become more challenging as Ashby grows across products, regions, systems, and business functions. We will need to develop stronger domain context and clearer ways of working without turning the team into a set of narrow, disconnected specialists.
Can you help the Data team navigate the rapidly evolving implications of: AI? AI will affect how our analysts work, how the company self-serves analytical questions, and how other teams use internal business data. Much of that work depends on trusted, well-modeled, appropriately accessible warehouse-derived data. You will help the Data team explore and evaluate the relevant tools, workflows, and partnerships—bringing ambition, practicality, and a high bar for accuracy.
Have exceptional analytical judgment. You can calibrate the depth and rigor: of an analysis to the decision at hand: knowing when precision matters, when uncertainty is material, and when additional work would not change the outcome.
Can produce and evaluate transparent, rigorous analysis. You bring readers: along through the problem context, data considered, assumptions, meaningful cleaning or reduction steps, caveats, statistical pitfalls, and practical conclusion.
What they're looking for
- Are a strong visual and written communicator. You care about clear tables and: charts, meaningful labels and definitions, coherent narratives, and making complex work understandable without overstating certainty.
- Have deep hands-on SQL experience, plus working fluency with dbt-style data: modeling, cloud data warehouses, and BI tools. You do not need to have owned a warehouse as a Data Engineer, but you should be comfortable working across the analytical data stack.
- Have led, managed, or functionally acted as a manager for data practitioners: in a high-growth company. Formal people-management experience is valuable, but not required if you have demonstrated equivalent judgment, coaching, hiring, and team-leadership abilities.
- Bring a product-manager-like instinct for finding the core need behind a: request, understanding users and business context, and turning ambiguous questions into useful work.
