Find data owners
Find data leaders by warehouse, team size, and hiring signal
A head of data appears once somebody has to answer for the numbers. Before that, analytics belongs to whichever engineer last built a dashboard and did not want the job.
The data function forms late and then forms fast. One quarter a company has a handful of dashboards owned by engineering. The next it has a warehouse, a first analytics hire, and someone whose title contains the word data. Catching that transition is the difference between a sale and a polite no.
Two things make the transition visible from outside. Postings name the warehouse, the transformation layer, and the reporting tools before any of it is public, and the team count moves from zero to three in a matter of months. TheirStack carries the first, People Data Labs the second, and BuiltWith covers whatever reaches the public site.
Example searches
Ask for it the way you'd say it
Every search below runs on the same hosted Tables, with the cost previewed before anything is spent.
The first data hire
which companies posted their first analytics engineer or data engineer role this year
A first hire in a function means the stack is being chosen right now, and the posting date on the row tells you how much runway you have.
Warehouse named in a posting
find companies whose job posts name a cloud warehouse and get whoever owns data there
The named tools land as columns on the account, so you can require one and exclude another before any person work runs.
Team size, not title
give me accounts with at least four people in data even if nobody holds a head of data title
Counting the function catches companies where the work is real and the title has not been created yet, which is most of them.
Plays
Three motions you can run this week
Each one is a chain of Oxygen primitives — the same hosted objects your workspace already has, composed.
Catch the stack decision early
You arrive while the warehouse and the tooling around it are still being chosen.
- 1Bind a hiring search for data and analytics roles across your target segment.
- 2Enroll accounts whose first such posting appeared inside your chosen window.
oxygen signals search runCount the function, ignore the title
Companies with a real data team surface even where nobody carries the title.
- 1Resolve everyone holding a data or analytics title and store the count per account.
- 2Cut below your floor, take the most senior person on each row, and enroll.
oxygen people search runTag by stack generation
One list segmented by how modern the data platform already is.
- 1Score each account by the generation of stack its postings describe and keep it as a column.
- 2Promote that value onto the company record, then tag the record so later reporting reads one value.
oxygen crm tag
Who you can reach
Roles you can find at heads of data and analytics
Reachability is per channel: a role marked for mobile is one the phone waterfall usually resolves, not a guarantee for every record.
| Role | Seniority | Reachable by |
|---|---|---|
| Head of Data | Director | Work emailLinkedIn |
| VP of Data | VP | Work emailLinkedIn |
| Director of Analytics | Director | Work emailLinkedIn |
| Analytics Engineering Manager | Manager | Work emailLinkedIn |
| Data Engineering Lead | Manager | Work emailLinkedIn |
| Chief Data Officer | C-level | Work emailLinkedInMobile |
| First data hire with no title yet | IC | LinkedInWork email |
Filters you can search on
- Warehouse or lake named in job postings
- Transformation and reporting tooling mentioned
- People holding data or analytics titles
- Open data roles posted this quarter
- Headcount band and funding stage
- Country, region, or metro area
- Seniority: C-level, VP, Director, or Manager
Postings are the earliest view of a data stack and the least complete one. A company can run a warehouse for two years without advertising for it once. Treat a named tool as strong evidence and a silent account as unknown, then let the team count act as the second opinion before you drop anything.
Data sources
What the data actually comes from
Every value lands with its provider and cost recorded on the cell.
TheirStack
Job postings and the data tools named inside them, which is where a stack choice becomes public first.
People Data Labs
Person and company records, used to count the data function and find whoever is senior in it.
BuiltWith
Technology detected on the domain, covering the reporting and tracking layer that faces outward.
LinkedIn Scraper
Public profile reads with no connected account, useful because this function posts about its work.
Blitz API
First attempt at the data owner's work email, and the LinkedIn company read the team count is assembled against.
Run these on Oxygen's managed credits, or connect your own provider keys and pay the vendor directly — the same columns, the same runs, the same provenance either way. See every integration.
Limits
Where this stops
- A posting names what a team wants to hire for, which is sometimes what it already runs and sometimes what it intends to buy next quarter. Both are useful and they are not the same fact.
- Data titles arrive late. Plenty of companies have three people doing the work under engineering titles, which is why counting the function beats matching the word.
FAQ
Questions people ask first
Why do job postings show the stack so well here?
What if nobody has the head of data title?
Is the first data hire worth targeting?
Can I exclude companies already on a stack I lose to?
How stale can posting data get?
Do I need to connect anything to research this?
Build your heads of data and analytics list
Start with a search, preview the cost before anything is spent, and keep the rows, sources, and runs in a workspace you own.