Senior Analytics Engineer

Kit is hiring a Senior Analytics Engineer to own sales and creator commerce data end to end. The role builds and maintains trusted dbt models, Omni dashboards, and semantic-layer documentation, partnering primarily with Sales and RevOps and also supporting Product reporting.

Responsibilities:

• First Week: Work through our onboarding curriculum in Notion and meet the data team, our Senior Revenue Operations Manager, the sales team, and your product partners. Get set up in our core tools: dbt Cloud, GitHub, Amazon Redshift, Omni, HubSpot, Linear, Hightouch and Slack. Set up your AI-assisted development environment and learn the shared AI workflows our data team uses. Open and merge your first small pull request so you learn our review and deployment flow early
• First Month: Map how HubSpot objects (deals, pipelines and stages, companies, contacts, and owners) flow into our dbt models, and document the gaps. Take over day-to-day ownership of the sales models and Omni dashboards that teammates have been covering. Audit current sales reporting and list where definitions differ or numbers don't reconcile. Get familiar with how commerce data (products creators sell and the revenue they earn through Kit) lands in the warehouse today. Agree with RevOps and Sales on the most important questions they need answered. Review teammates' pull requests and contribute to our shared SQL, dbt, and documentation standards. Share a written reflection with the team on what you've learned and where you'd focus first
• First Six Months: Ship and maintain well-tested, documented dbt models for core sales metrics, such as pipeline, deal velocity, stage conversion, win rates, and the sales-led funnel. Expose those metrics through the Omni semantic layer, working within the team's governance standards. Rebuild or refresh the core sales dashboards so pipeline reviews and forecasting conversations run on numbers that don't need manual reconciliation. Resolve competing sales metric definitions by writing down the options and tradeoffs, aligning with Sales and RevOps, and documenting the outcome. Model creator-level commerce data, such as products sold, revenue earned through Kit, and conversion, in partnership with data engineering. Write semantic layer descriptions, metric definitions, and documentation that let Omni's AI and MCP-connected AI tools answer routine sales questions correctly, and test them against real questions from Sales, RevOps and Product. Get Sales to the point where routine questions are answered through self-serve, whether in Omni dashboards, Omni's AI, or AI tools connected over MCP, rather than one-off requests
• First Year: Be the recognized owner of Kit's sales data, the person Sales and RevOps go to first and trust without second-guessing. Give our revenue analysis work the sales and commerce models it depends on, so deeper questions about pipeline health and creator earnings can be answered quickly, thereby enabling us to make creators more successful. Leave your domains well documented and well tested, legible to new teammates, stakeholders, and the AI agents that query our semantic layer. Help raise the team's modeling quality through code review, pairing, and sharing what you learn

Requirements:

• Deep dbt experience. You build modular, tested, documented dbt projects, and you're comfortable with incremental models, snapshots, macros, CI, and scheduled jobs in dbt Cloud or similar
• Advanced SQL. You reason carefully about grain, joins, and window functions, and you write queries that are readable and performant on a cloud warehouse
• Data modeling for sales and GTM. You can model deals, pipeline stages and their history, ownership changes, and funnel conversion into clean, reusable structures. You are opinionated on Kimball standards and when a concept should be a fact or dimension
• You know how sales data behaves in a CRM, including stage changes, backdated edits, and custom properties
• You use AI code editors and agents (such as Cursor, Claude Code, Codex, or similar) every day, and you validate their output with tests, query results, and review before anything ships
• Clear written communication. You explain metric definitions, tradeoffs, and data caveats in plain language that Sales, RevOps, and Product partners understand
• BI and semantic layer experience. You can build dashboards and semantic models in Omni, Looker, or a similar BI tool, and you design metric definitions that people and AI tools can both query correctly
• Git-based workflows. You keep pull requests small and well described, and you give thoughtful code review
• Five to six years in analytics engineering or data engineering where you personally shipped production data models
• Several years of hands-on work building and maintaining production dbt projects
• Working directly with sales teams and RevOps, and owning reporting they relied on to run the business
• Owning a data domain over time and evolving its models as the business and its questions changed
• Working at a SaaS company, ideally one with both a self-serve and a sales-led motion
• Working on a small or remote team where you were trusted to make decisions independently
• Ownership and follow-through. You treat your data domains as yours. You notice problems before stakeholders do, close the loop on requests, and finish what you start
• Judgment. You match rigor to the decision at hand, know when a quick answer is enough and when a request should become a durable model, and say so clearly
• Flexibility. You're glad to step outside your primary area when another team needs help, and you balance that work against your core commitments openly
• Teach everything you know. You document as you go, pair generously, and help stakeholders understand the data well enough to use it on their own
• Work in public. You share progress, open questions, and decisions in writing where the team can see them, including what didn't work
• Creator success is our success. You remember who's on the other end. A wrong number in a pipeline review means Sales spends time on the wrong creators, and that shapes how carefully you work
• Do it with excellence. You test your work, review it carefully, and don't ship a number you can't defend
• Candor with care. You're comfortable pushing back when a request won't answer the real question, and you do it in a way that builds trust
• HubSpot is preferred, but other CRM experience (e.g. Salesforce) applies just as well
• Nice to have: Redshift performance tuning (sort and distribution keys), Python for scripting or API pulls, reverse ETL, and experience with product event, billing, payments, or ecommerce data
• Commerce, payments, or creator economy experience is a plus, not a requirement