Senior Analytics Engineer
New
K
KitEmail marketing
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.Full-TimeSenior
SalaryFor this role, we're hiring at our level 4 ($172,000).
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Job Details
- Experience
- Around 7 or more years in analytics engineering or data engineering where you personally shipped production data models.
- Required Skills
- SQLGitHubSpotdbt
Requirements
- Around 7 or more years in analytics engineering or data engineering, personally shipping production data models.
- Several years of hands-on experience building and maintaining production dbt projects.
- Deep dbt experience, including modular, tested, documented projects, incremental models, snapshots, macros, CI, and scheduled jobs in dbt Cloud or similar.
- Advanced SQL, including careful reasoning about grain, joins, and window functions, and writing readable, performant queries for a cloud warehouse.
- Experience modeling sales and go-to-market data, including deals, pipeline stages and history, ownership changes, and funnel conversion.
- CRM fluency, including common patterns such as stage changes, backdated edits, and custom properties; HubSpot is preferred, and other CRM experience such as Salesforce is applicable.
- Daily use of AI code editors and agents, with validation through tests, query results, and review.
- Experience building dashboards and semantic models in Omni, Looker, or a similar BI tool.
- Git-based workflow experience, including writing well-described pull requests and conducting code reviews.
- Experience working directly with Sales and RevOps and owning reporting they rely on.
- Experience owning a data domain over time and evolving its models as business questions change.
- Experience working at a SaaS company; experience with a self-serve and sales-led motion is ideal.
Responsibilities
- Own and maintain dbt models for sales metrics, including pipeline, deal velocity, stage conversion, win rates, and the sales-led funnel.
- Expose sales metrics through the Omni semantic layer within the team's governance standards.
- Rebuild or refresh sales dashboards used for pipeline reviews and forecasting.
- Align with Sales and RevOps on metric definitions and document decisions and tradeoffs.
- Model creator-level commerce data, including products sold, revenue earned through Kit, and conversion, in partnership with data engineering.
- Write semantic-layer descriptions, metric definitions, and documentation for Omni and MCP-connected AI tools.
- Test AI-accessible definitions against real questions from Sales, RevOps, and Product.
- Support Product reporting, including feature usage reporting.
- Review pull requests and contribute to shared SQL, dbt, and documentation standards.
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