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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** K.I.T. & COMPANY, INC. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $172,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Big Data, Software as a Service, Cloud Computing, Software Documentation, Code Review, Customer Data Management, Information Engineering, Extract Transform Load (ETL), Cursor, Github, Python (Programming Language), Operational Data Store, Operational Databases, Performance Tuning, SQL Databases, Macros, Claude Code, Agentic-AI, Git Flow, Virtual Agents, Hubspot, Looker Analytics, Automation Anywhere, Amazon Redshift - **Published:** November 10, 2026 - **Apply:** https://www.builtincolorado.com/job/senior-analytics-engineer/12153222?handler=ApplyRedirect ## About the Role * 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. * CRM data fluency. You know how sales data behaves in a CRM, including stage changes, backdated edits, and custom properties. HubSpot is preferred, but other CRM experience (e.g. Salesforce) applies just as well. We care most that you recognize common CRM patterns. * AI-assisted development. 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. * 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. Experiences * 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. ## Description 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. What you'll do * 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., * 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. What won't set you up for success * Needing detailed specs or a highly structured environment before you can start. * Discomfort with ambiguity. Some of our sales and commerce definitions are still evolving, and you'll help settle them. * Needing several layers of approval to make modeling decisions within your domain. * Wanting to work only on dashboards, or only on pipelines, rather than across the transformation and BI layers. * Wanting a strictly fixed scope. Sales comes first, but you'll pick up Product or other work when the business needs it. * Wanting mostly greenfield work. Much of this role is improving sales models and dashboards that already exist. * Preferring to take tickets as written rather than partnering with stakeholders on what they need. * Being skeptical of AI's capabilities, or using AI without good judgment and accountability. * Expecting a large data team with dedicated specialists for every part of the stack. 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. Compensation + perks + benefits