Kit Senior Analytics Engineer at Kit to own sales and commerce data modeling and reporting. Build dbt models and dashboards to provide trusted pipeline and revenue metrics.
Responsibilities
Our Sales team needs a dedicated data partner, and creator commerce (how creators monetize by selling digital products) is becoming a bigger part of how Kit and our creators scale. Kit is growing, so we're looking for a Senior Analytics Engineer to own Kit's sales and commerce data from end to end.
Accurate sales data gives our team a clear picture of the pipeline and the confidence to make informed decisions. That’s what makes this role so important.
Your primary focus will be Sales: the dbt models that turn our CRM data into trusted pipeline, deal, and funnel metrics, and the Omni dashboards our sales leader and Senior Revenue Operations Manager use to run the business. You'll also model creator commerce data, so we understand what creators sell and earn through Kit, and you'll pick up reporting work with our Product team, such as feature usage reporting. On a small data team, we step outside our usual lanes when the need is there, so the mix will shift over time.
You'll build for two kinds of consumers: people and AI. Teammates increasingly answer their own questions by querying our semantic layer through Omni's AI and MCP connections to AI tools. That means your models, metric definitions, and documentation need to be clear and correct enough for an AI agent to use them without a data teammate in the loop.
This is a full-time individual contributor role for someone who likes being close to the business, enjoys turning messy operational data into numbers people trust, and is happy to work on both the models and the dashboards that sit on top of them. When Sales and Product have clear, reliable data, we help more creators build more valuable businesses with Kit.
Your support system
You'll report to Sam Umachi, Head of Data (Linkedin).
Sales and RevOps are your primary partners, who will rely on the models and reporting you own. You'll also partner with our Product team on reporting for features like Visual Automations and on how creators use Kit.
Within our data team, you'll work alongside a Staff Analytics Engineer who owns org-wide semantic layer governance and modeling standards, a Senior Data Engineer who owns ingestion and pipelines, and a Senior Business Analytics Partner focused on revenue opportunities. This role has no direct reports.
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.
Qualification
Working at a SaaS companyOwnership and follow-throughJudgment
Preferred
Around 7 or more 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. Commerce, payments, or creator economy experience is a plus, not a requirement.
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.
