kit - Staff Analytics Engineer

• Deep SQL mastery. You write queries that are readable, performant, and designed for maintainability. You understand relational semantics deeply, including join selection, cardinality management, and predicate placement. • Redshift expertise. You know how sort keys, distribution styles, concurrency, vacuum, and analyze behavior work, and how to design around their constraints. • dbt fluency. You build modular, well-documented, refactorable dbt code. You have opinions about macro design, test strategy, and what “good” looks like in a dbt project at scale. • Business modeling strength. You can translate revenue recognition logic, attribution frameworks, funnel stages, and lifecycle metrics into reliable, auditable models that stakeholders trust. • Written communication. You write clear architectural memos, modeling documentation, and decision rationale. You communicate trade-offs, not just conclusions. • Analytical judgment. You have experience navigating contested metric definitions, challenging dashboard results, or attribution debates while maintaining analytical rigor. • Stakeholder translation. You can explain complex metrics and modeling decisions to different stakeholders and communicate in terms they trust. • 7–10+ years working in analytics engineering, data engineering, or a closely related role, with at least a few years operating at a senior or staff level. • Designed or significantly refactored a warehouse modeling layer in a production environment. • Built or stewarded canonical metric definitions in a multi-stakeholder environment. • Worked in a SaaS company where revenue recognition, churn logic, or subscription modeling was a core part of the work. • Experience with product analytics modeling, Segment event architecture, identity resolution, or cohort/retention modeling is a strong plus. • Collaborated with infrastructure engineers to address performance challenges at the warehouse level. • As a lead level IC, you see your role as amplifying the effectiveness of the entire data team and driving company wide impact. You lead by example. • You think in systems. When you look at a modeling problem, you see the upstream causes and downstream consequences, not just the immediate fix. • You’re patient with complexity. You don’t oversimplify hard problems, and you don’t ship a definition you can’t defend. • You document as you go, not as an afterthought. You believe that work that’s not written down didn’t really happen. • You push back when necessary. You’re comfortable disagreeing with stakeholders, explaining your reasoning, and holding the line on rigor when speed would create long-term problems. • You use AI tools actively. You’re comfortable using AI to accelerate drafting, exploration, and deployment, while applying your own judgment to the outputs. • You operate with extreme autonomy. At a Staff level, you don’t wait for a detailed brief. You identify what needs to be done, communicate your plan, and move. • WHAT WON'T SET YOU UP FOR SUCCESS • Defaulting to infrastructure scaling when the real problem is modeling design. • Preferring to build tools or processes before understanding the business context they’re meant to serve. • Optimizing in isolation, without documenting decisions, communicating trade-offs, or looping in collaborators. • Discomfort with ambiguity. Many of our metric definitions are still evolving. You’ll need to lead the process of resolving them, not wait for resolution before acting. • A preference for being told what to build rather than helping define what should be built. • WORKING HOURS AND LOCATION • We’re 100% remote with team members across the globe. We work mostly on East Coast time, so if you’re based on the West Coast of the United States, Europe, or Asia, you might have a few early mornings or late evenings. We do our best to keep things reasonable, but a little time zone juggling comes with the territory.