AdTechTalent

Another small update on the AdTech jobs platform.

I’ve now finished normalizing most of the job data, and I’m honestly pretty happy with how it’s turning out.

Getting consistent structure across:

• locations

• job titles

• salary formats

• tech signals

was more challenging than expected, but it’s starting to feel solid.

Now shifting focus to:

• adding more sources and companies

• increasing job coverage

• improving the layout and overall UX

Feels like I’m moving from “making the data usable” to “making the product actually useful”.

Still early, but definitely a good milestone.

I recently analyzed ~1,500 active job listings across the AdTech ecosystem, and a few patterns stood out:

• Demand is strongest for Sales and Engineering roles (658 openings)
• Flexible work is now the norm, with 61% of roles offering remote or hybrid options
• Python, SQL and Excel are consistently listed across many positions
• A small group of companies (StackAdapt, Smartly, The Trade Desk) account for a significant share of open roles

What I found most interesting is that, despite all the talk about AI and automation, there’s still strong demand for people who understand how things actually work under the hood — delivery, measurement, and performance.

Also, hiring across AdTech remains highly fragmented across ATS platforms, company pages and LinkedIn, which makes it surprisingly hard to get a clear picture of the market.

Here there is more detail about it: https://adtechtalent.com/top-adtech-companies-hiring

After normalizing more data, I’m starting to see some interesting patterns across companies.

A few things that stand out:

• The same role can have 5–6 completely different titles depending on the company

• Remote vs hybrid is often inconsistent or unclear in job descriptions

• Salary transparency is still very uneven (some companies are very explicit, others give nothing)

• Tech stack signals are usually buried in long descriptions rather than structured fields

Also interesting: even when companies use the same ATS, the way they structure jobs is all over the place.

It’s making me think that the real challenge here isn’t just aggregation, but building a layer that makes this data actually comparable.

Still early, but starting to see how this could become useful beyond just listing jobs.

I’ve been focusing mostly on scrapers and data normalization, and one thing that turned out to be more complex than expected is standardizing job data across companies.

Things like location (city vs city + state vs remote vs hybrid), salary formats (ranges, currencies, missing data), tech stacks embedded in descriptions are really different from one to the other.

Even companies using the same ATS (Greenhouse, Lever, etc.) structure things quite differently.

Feels like a big part of the value here will actually come from cleaning and structuring the data, not just aggregating it.

Still early, but starting to see where the real complexity (and opportunity) is.

Spent the last few days working on a specialized jobs platform focused on the AdTech ecosystem.

Right now I’m mainly building:

• job scrapers

• company pages

• search/filtering

• structured job data

• SEO foundations

One thing that surprised me already is how fragmented AdTech hiring still is across ATS platforms, LinkedIn and company career pages.

Still very early, but fun project so far.