SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Prefect is hiring a Senior Analytics Engineer to own go-to-market analytics and data modeling. You will report to the Chief Growth Officer and work cross-functionally with marketing, sales, RevOps, and product teams.
In this role, you will:
- Own the complete go-to-market data model, establishing single definitions for accounts, activated workspaces, and pipeline. Your models will be version controlled, tested, documented, and deployed in production.
- Model both self-serve and sales funnels end-to-end for Prefect Cloud and Dagster+, tracking progression from first touch through signup, activation, paid conversion, and expansion. You'll make drop-off points visible and enable cohort comparisons.
- Build attribution models (first touch, last touch, multi-touch) that marketing and sales both accept, with documented assumptions so strategic discussions focus on strategy rather than arithmetic.
- Develop unit economics including CAC and payback by channel/segment, LTV/CAC ratios, campaign ROI, and self-serve vs. sales-assisted comparisons. This includes sourcing spend data that hasn't been loaded yet.
- Create a semantic layer and reporting infrastructure (using Omni or Looker) so go-to-market teams can answer their own questions independently.
- Shape the go-to-market rhythm by participating in pipeline reviews, weekly funnel meetings, and board reporting. You'll often reframe questions before they become tickets.
Required qualifications: 4+ years building analytics models in dbt on cloud warehouses with production ownership (tests, CI, version control). Strong SQL and Python for API pulls, spend data enrichment, and one-offs. Experience building semantic layers in Omni, Looker, or equivalent. Direct GTM experience with funnel conversion, attribution, CAC, payback, and ROI using real Salesforce or HubSpot data. Familiarity with GTM systems (Salesforce, Amplitude, Segment, HubSpot). Background in product-led or self-serve businesses. Engineering or data background with code-writing and deployment experience. Comfort with AI-native development.
Nice-to-have: Product analytics tools (especially Amplitude), developer tools or open-source experience.
This role is not for those seeking machine learning/data science work, expecting clean data, wanting to take tickets with specifications, or planning to manage a team soon. It's an individual contributor role focused on modeling and measurement for go-to-market decisions.
