Pay
$122K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 321 postings in the last year. Stated ranges, not salaries paid.
San Francisco · San Francisco, California, United States
Actively hiring, first seen by us todayLocation type: HybridFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: DataFrom the employer’s posting
Will you now or in the future require sponsorship to maintain employment at Scribe?From the employer’s application form
As the Lead Analytics Engineer for GTM, you’ll be the first dedicated data hire for our go-to-market organization, reporting directly to the Head of Data.
Working hand in hand with sales, marketing, finance, and RevOps, you will answer Scribe’s most critical go-to-market questions and turn them into durable, self-serve data products.
Design first-principles GTM data models across Sales, Marketing, and Finance
Turn ambiguous business questions into simple, actionable metrics and dashboards
Serve as the go-to resource for GTM data questions and deep-dive analysis
Build documentation, data quality standards, and self-serve tooling for GTM
Mentor analysts and analytics engineers as the GTM team grows
As the Lead Analytics Engineer for GTM, you’ll be the first dedicated data hire for our go-to-market organization, reporting directly to the Head of Data. You’ll design and build the core data models that define how Scribe measures pipeline, bookings, revenue, and customer growth, and you’ll set the standards that future GTM analytics work is built on. As the team grows, you’ll have the opportunity to mentor and shape the analysts and analytics engineers who join it.
Working hand in hand with sales, marketing, finance, and RevOps, you will answer Scribe’s most critical go-to-market questions and turn them into durable, self-serve data products. Your work will be central to how we scale our enterprise motion, helping leadership understand what drives pipeline and revenue, and where to invest next.
Build Scribe’s foundational GTM data models from first principles
Turn ambiguous business questions into simple, actionable metrics
serving as the go-to resource for GTM data questions, deep-dive analysis, and technical guidance
through reproducible work, documentation, data quality standards, and self-serve tooling
Mentor and help grow the team as GTM analytics scales
From the employer’s posting.
Join us in our mission to transform how people work.
In the employer’s words · Read in context · Company website ↗
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$122K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 321 postings in the last year. Stated ranges, not salaries paid.
Day 0
Early. Similar jobs usually come down between day 6 and day 47.
From 1,405 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Have built data models from the ground up, and know how to design for clarity, scale, and trust rather than inheriting what already exists - Strong technical proficiency in our tech stack – Snowflake, dbt cloud, Sigma, Mixpanel, and Looker – with expert SQL and data modeling skills - Fluent in GTM and revenue data, including CRM, marketing, and billing systems, and concepts like pipeline, bookings, ACV/ARR, funnel conversion, and attribution
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We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
About Scribe
Scribe is where exceptional people come to do the best work of their careers. More than 94% of the Fortune 500 use Scribe to own their specialized intelligence: the unique way their teams work, decide, and get things done. Our Specialized Intelligence platform automatically captures how work happens and turns it into a living asset for people and AI agents.
We're growing fast. Since our founding in 2019, we've reached 7 million users across 600,000 businesses. Based in San Francisco, we're a LinkedIn Top Startup, valued at over $1B, and backed by leading investors. Join us in our mission to transform how people work.
tl;dr – Why this role matters
At Scribe, data is the foundation of how we make decisions — we take it very seriously. As we accelerate our sales organization into a first-class enterprise team, the data behind our go-to-market motion needs to be just as strong as the data behind our product. We’re looking for a hands-on builder to lay the foundation of GTM data at Scribe — designing first-principles data models across Sales, Marketing, and Finance, and building a culture where every GTM decision starts from a trusted, shared source of truth.
Hiring Manager: Melanie Herscher, Head of Data
About the role
As the Lead Analytics Engineer for GTM, you’ll be the first dedicated data hire for our go-to-market organization, reporting directly to the Head of Data. You’ll design and build the core data models that define how Scribe measures pipeline, bookings, revenue, and customer growth, and you’ll set the standards that future GTM analytics work is built on. As the team grows, you’ll have the opportunity to mentor and shape the analysts and analytics engineers who join it.
Working hand in hand with sales, marketing, finance, and RevOps, you will answer Scribe’s most critical go-to-market questions and turn them into durable, self-serve data products. Your work will be central to how we scale our enterprise motion, helping leadership understand what drives pipeline and revenue, and where to invest next.
Key responsibilities of this role include:
Build Scribe’s foundational GTM data models from first principles, starting with Sales and extending across Marketing and Finance, so that metrics like pipeline, bookings, ARR, and conversion have one clear, consistent definition.
Turn ambiguous business questions into simple, actionable metrics, partnering directly with GTM and finance leaders to define the KPIs, dashboards, and analyses they run the business on.
Be a cross-functional magnet with our sales, marketing, and finance teams, establishing trusted partnerships and serving as the go-to resource for GTM data questions, deep-dive analysis, and technical guidance.
Lay the foundation of GTM data culture through reproducible work, documentation, data quality standards, and self-serve tooling that make data accessible to everyone in the GTM org.
Mentor and help grow the team as GTM analytics scales, setting the technical bar and best practices for the analysts and analytics engineers who follow.
What makes you a great fit
You’d be a great fit for this role if this sounds like you:
Have 8+ years of experience in analytics engineering, data engineering, or analytics, with significant time spent on Sales, Marketing, or Finance data at a SaaS company
Have built data models from the ground up, and know how to design for clarity, scale, and trust rather than inheriting what already exists
Strong technical proficiency in our tech stack – Snowflake, dbt cloud, Sigma, Mixpanel, and Looker – with expert SQL and data modeling skills
Fluent in GTM and revenue data, including CRM, marketing, and billing systems, and concepts like pipeline, bookings, ACV/ARR, funnel conversion, and attribution
Customer mindset, adept at understanding how sellers, marketers, and finance partners actually work, and communicating complex findings clearly to non-technical audiences
Low ego. You care about building things and recognize that this may require rolling your sleeves up, getting in the trenches, and owning everything from data modeling to ad-hoc analysis
Proactive, high-output orientation. You push insights and data onto teams rather than waiting for requests to emerge from them
Outcomes over output. You thrive on creating business value and know when to build a durable model v/s doing ugly napkin math to bias towards action
Experience building technical and semantic infrastructure to power internal natural language agents and agentic automation
Bonus Points
Experience working in high-paced Enterprise SaaS startups, especially during a shift from self-serve to enterprise sales
Experience mentoring analysts or analytics engineers, or serving as a tech lead on a data team
Experience creating a step-function change in a GTM team’s effectiveness with the help of AI tooling
Benefits
Healthcare Coverage – Comprehensive health, dental, and vision plans, including two $0/month medical plan options for employees
Equity – Ownership in what we're building
401(k) Plan – through Vestwell
Flex Benefit – $500/year for home office equipment, productivity tools, learning & development or fitness/wellness
Commuter Benefits – $100/month for SF based employees
PTO – Flexible paid time off and company holidays
Parental Leave – Paid leave to support growing families
Wellness & Family Support – Free Talkspace membership, One Medical access (location-dependent), and Kindbody discounts for family planning
At Scribe, we celebrate our differences and are committed to creating a workplace where all employees feel supported and empowered to do their best work. Scribe is proud to be an Equal Opportunity Employer.
Our Worker Privacy notice can be found here.
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San Francisco, California, United States
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