About this GTM Data Analyst role at Netic
Netic is the AI revenue engine for essential services who are the backbone of the American economy. With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America. There are now companies operating entirely AI-first on Netic.
You’ll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible.
The role
You'll be Netic's first dedicated go-to-market data hire. Nobody owns our account and contact data end to end yet: you will. You'll define the data standards, build the models and checks that keep them accurate, and give the team a foundation for territories, routing, forecasting and commissions.
This is an analyst role with operational ownership. You'll spend as much time writing SQL and building models as you do fixing the processes that create bad data in the first place.
What you'll do
Own the GTM data model: define account, contact and hierarchy standards (parent companies, multi-location operators, PE-backed roll-ups), required fields at each stage, and the rules for creating, matching and merging records.
Build our total addressable market model: map every home services business by trade, geography and location count, score it, and keep it in sync with Salesforce.
Design and run enrichment workflows that keep firmographics and decision-maker contacts current, and measure the coverage and accuracy of each data source.
Define data quality metrics (duplicate rate, field completeness, match rate) and build the dashboards and alerts that catch bad data before it reaches a forecast or a commission statement.
Maintain the data behind territory assignments and lead routing, and give Sales Ops the evidence to resolve ownership disputes.
Run analyses that answer GTM questions: which segments convert, where coverage is thin, and which accounts we're missing.
Automate recurring cleanup and validation with our Sales Systems engineer, so hygiene runs as a system instead of a project.
What success looks like in year one
A documented standard for accounts, contacts and hierarchies that every team follows.
A TAM model the sales team uses to plan territories and prioritize accounts.
Data quality metrics tracked weekly, with duplicate rates and field gaps trending down.
Recurring cleanup automated rather than done by hand.
What you bring
2+ years in sales operations, revenue operations, marketing operations or data analytics at a B2B company.
Strong SQL and advanced Excel or Google Sheets; Python is a plus. You're comfortable building a model or analysis from raw, messy data.
Hands-on Salesforce data experience (Data Loader, Workbench or a dedupe tool) and a solid grasp of the object model.
A detail-first mindset. You notice when a number looks off and you chase it to the root cause.
The ownership to define standards, not only follow them, and the documentation habits to make them outlast you.
Nice to have
Experience with ZoomInfo, Clay, Apollo, LeanData, Cloudingo or DemandTools.
Familiarity with BI tools (Looker, Tableau, Hex) or a data warehouse (Snowflake, BigQuery).
Experience using AI coding assistants to automate data work.
Our values
Obsess over the customer
Every second counts
Sweat the details
There are no blockers
Be self-directed
Seek and speak the truth
Enjoy the ride
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
