Senior Analytics Engineer

Prefect

10d•$154,000 - $222,000•Remote

About The Position

Prefect builds and operates resilient, Pythonic orchestration and MCP platforms -- Prefect OSS, Prefect Cloud, FastMCP OSS, and Horizon -- used for mission-critical workloads. Our Vision: Prefect will define automation for the context era. Our Mission: Curate an intelligent context layer that delivers the right information at the right time. About Prefect: Prefect builds automation for an unpredictable world. The last decade of automation was about protecting workflows from unpredictability; in the agentic era, unpredictability is the point. Our mission is to give people confidence in automated work, whether that work is a mission-critical data pipeline or a fleet of AI agents. In August 2026, Prefect and Dagster, competitors for eight years, joined forces to build the next generation of automation infrastructure. Today, our ecosystem includes Dagster, the data platform; Prefect, the agent platform; and FastMCP, the leading open-source developer framework for the MCP ecosystem. Together, our open-source and commercial products are trusted by Fortune 500 companies, data innovators, and high-growth technology companies around the world. We think of our culture as the operating system of the company. We’ve carefully created a supportive, high-performance environment that empowers our team to do the best work of their careers, have meaningful impact, and continue growing personally and professionally. We’re a remote-first team that values high standards, ownership, and thoughtful collaboration. Role Summary: We sell two products with two funnels that behave nothing alike. Prefect Cloud and Dagster+ both start with someone running an open source tool on their laptop, and both end either in a self-serve credit card or in a sales conversation with a Fortune 500 data platform team. Between those two points sits our go-to-market data: product telemetry in Amplitude, the warehouse, Salesforce, campaign and channel spend, community signals, and an uncomfortable number of different systems and touch points. You will own the analytics and data modeling for GTM that describe how someone gets from first touch to activated user to paying customer, across both self-serve and sales. You will report to Gabriel Madureira, our Chief Growth Officer, and work day to day with marketing, sales, RevOps, and product.

Requirements

Four or more years building analytics models in dbt on a cloud warehouse, owned in production with tests, CI, and version control.
Strong SQL, plus enough Python for the parts SQL is bad at: API pulls, spend data, one-off enrichment.
Experience building and maintaining a semantic layer in Omni, Looker, or something comparable. We care that you have modeled metrics for other people to use, not that you have built dashboards.
Direct work with go-to-market teams on funnel conversion, attribution, and unit economics like CAC, payback, and ROI, using real CRM data from Salesforce or HubSpot.
Prior knowledge of GTM systems like Salesforce, Amplitude, Segment, Hubspot.
A product-led or self-serve business: activation, trial conversion, PQLs, usage-based revenue.
A background in engineering or data, meaning you write code, read other people's code, and deploy your own work.
Comfort with AI-native development.

Nice To Haves

Product analytics tools, especially Amplitude.
Developer tools or open source, where the funnel starts with a pip install and the top of it is a community rather than a lead list.

Responsibilities

Own the go-to-market data model. One definition of an account. One definition of an activated workspace. One definition of pipeline. Version controlled, tested, documented, and deployed by you.
Model the self-serve funnel end to end, from first visit through signup, activation, paid conversion, and expansion, for both Prefect Cloud and Dagster+. Make the drop-off points visible and the cohorts comparable.
Model the sales funnel end to end, from source through MQL, SQL, pipeline, and closed won, including stage conversion and velocity, using Salesforce data as it actually is rather than as the schema claims.
Build attribution that marketing and sales both accept. First touch, last touch, and a multi-touch model, with the assumptions written down so the arguments are about strategy instead of arithmetic.
Build the unit economics. CAC and payback by channel and segment, LTV/CAC, ROI on campaigns and events, self-serve versus sales-assisted. That includes going and getting the spend data nobody has loaded yet.
Ship the reporting layer so go-to-market teammates answer their own questions instead of queuing behind you. We run two Omni instances, one per product. Looker experience transfers directly.
Help us set the go-to-market rhythm. Pipeline reviews, the weekly funnel meeting, board reporting. You'll hear a question before it becomes a ticket, and often you'll be the one who reframes it.