GTM Analytics Engineer
Decagon is hiring a GTM Analytics Engineer in San Francisco, United States. It pays $190k-$230k a year and Level rates it ; you can apply on Decagon.
Level's read on this job
- Level rates this role Little AI ●○○○: aI is not part of the work.
- Tools and skills the posting cites include Gong, BigQuery and Salesforce.
- Level classifies it as a mid-level role.
- Level lists 133 other open roles at Decagon.
AI in this role
Build and maintain foundational go-to-market data infrastructure, data pipelines, and modeling layers for a conversational AI platform.
About Decagon
Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.
About the Team
Decagon’s Revenue Operations team is a multidisciplinary group of operators combining strategic thinking, analytical rigor, and high-velocity execution. Covering GTM-wide strategy, operations and analytics, we are proactive, data-driven partners to leadership – providing critical insights, driving operational efficiency, and getting things done.
About the Role
Decagon is looking for its first GTM Analytics Engineer to build the data infrastructure that our entire go-to-market organization runs on. You'll own turning that raw, messy data into clean, well-modeled tables that the rest of RevOps, sales leadership, and the exec team can actually build on. This is a true 0-to-1 role with support from the broader RevOps & GTM team: you'll design lots of data structures and modeling layers from the ground up, set the standards for how GTM data gets structured, and become the trusted person to answer "where does this number actually come from."
In this role, you will
Design and build the foundational GTM data infrastructure in BigQuery — ingesting, organizing, and modeling data from Salesforce, Gong, Outreach, and other GTM systems into a reliable data lake
Write and maintain models that transform raw CRM and GTM tool data into clean, trusted tables for pipeline, forecasting, segmentation, comp, and territory reporting
Partner with sales leadership and cross-functional partners to translate ambiguous, ad hoc reporting requests into durable, well-documented data models rather than one-off queries
Build and maintain data pipelines that keep GTM data fresh, accurate, and consistent as new tools and data sources get added
Own data quality end-to-end — establishing testing, validation, and monitoring so the numbers in Hex are numbers people trust
Set technical standards and best practices for GTM data modeling as the function scales
Your background looks something like this
4+ years of experience in data/analytics engineering, with hands-on ownership of ETL/ELT pipelines and dbt in a production environment
Strong SQL skills and direct experience building and maintaining data warehouses in BigQuery (or a comparable cloud warehouse)
Real experience working with GTM data — Salesforce is a must, plus familiarity with tools like Gong, Outreach, or similar sales engagement platforms in a high-growth B2B company is a plus
Strong grasp of core SaaS and GTM metrics — conversion rates, ARR/NRR, win rates, sales cycle length, quota attainment — and how they're derived from the underlying GTM tool data
Comfort turning messy, inconsistent source data into clean, well-organized, documented tables built for downstream reporting and dashboarding
Experience creating outputs, including dashboards, ad hoc analysis with large datasets in BI tools (Hex preferred) for dashboarding and self-serve analytics
A builder mindset — you're excited to build foundational infrastructure from scratch in a fast-moving environment rather than maintain an existing system
Strong cross-functional communication skills; you can work directly with sales leadership to understand what they actually need, not just what they ask for
Benefits
We proudly offer the following benefits for our full-time employees:
Medical, Dental, and Vision benefits for you and your family
Life Insurance and Disability Benefits
Retirement Plan (e.g., 401K, pension)
Parental Leave
Fertility and family building benefits through Carrot
Monthly stipend to support your wellness, lifestyle, and work-life balance
Daily lunches and snacks in the office to keep you at your best
Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)
These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
How we rate this
GTM Analytics Engineer at Decagon rates 20 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where analytics engineering was part of your work. What did you do?
- Tell me about a project where sql was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Data Engineering, Analytics Engineering, SQL, and Data Modeling. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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