Data Jobs Hiring Report — June 2026

Hiring is growing, and the spread between functions is the real story

The data job market added a net 624 roles over the last 28 days — 1,952 opened against 1,328 closed. That's not a churn artefact; openings outpaced closures by a comfortable margin, and the count of live, direct-apply postings now sits at 4,251 across 1,933 companies. After a few editions where I kept hedging on whether momentum was real, this one is clear enough: hiring is expanding, not just recycling.

What caught my eye more than the headline number is how concentrated the growth is among newcomers. In the last four weeks, 382 companies posted their first data role in this dataset. That's roughly one in five of all companies hiring right now opening a data function — or at least a data hire — for the first time that I've recorded. New entrants tend to post in ones and twos, which is exactly what the fastest-hiring table shows: nobody is opening 40 reqs at once. The growth is broad and shallow rather than driven by a handful of giants.

All numbers here come from postings scraped directly off company Greenhouse, Lever and Ashby boards — no aggregator noise, no reposts from staffing firms. If you want the live view, it's on the data jobs board.

Engineering and analytics split the market almost down the middle

Function breakdown is the cleanest signal in this edition. Engineering accounts for 2,206 roles and data analytics for 1,925 — together that's 4,131 of 4,251, or about 97% of everything. The long tail is genuinely a tail: 93 "other," nine product, seven operations, a scattering of sales and retail. So when someone asks me what a "data job" is in practice right now, the honest answer is that it's either a data engineering role or an analytics/science role, and very little else.

The near-even split between the two camps is worth sitting with. A few editions back, engineering pulled clearly ahead; now analytics is within striking distance. That tells me the demand isn't only for people who build pipelines and platforms — there's roughly matching pull for people who actually interrogate the data once it's flowing. If you're an analyst or scientist who's been told the market only wants engineers, the numbers don't support that fear.

One caveat on my own taxonomy: "data engineering" here lives inside the broader engineering bucket, so that 2,206 figure isn't all DE specialists — it includes adjacent platform and infrastructure work. Treat it as the upper bound on engineering-flavoured data roles, not a pure count.

Seniority skews experienced — junior openings are scarce

This is where the data turns blunt. Senior roles number 1,713 and principal another 433. Junior? 125. Director-level adds 98, executive 13, VP a mere 4. A further 1,865 postings don't specify a level at all, which is the single largest group and a reminder that a lot of companies still write job titles without a seniority tag.

Even setting the unspecified pile aside, the ratio of senior-plus to junior is brutal — well over ten to one. If you're early in your career, this market is not built for you, and pretending otherwise would be dishonest. The roles that exist assume you already know the tools and can operate without much hand-holding. My read: the "unspecified" bucket is where a chunk of the mid-level work hides, because companies posting a generic "Data Engineer" or "Data Analyst" title without a Senior/Junior prefix are often hiring for the experienced-but-not-staff middle. Worth applying to those even if you can't tell the level from the title — half the time neither can the recruiter.

For people tracking who's moving up and which companies are scaling teams, I keep a running view at data movers.

What data jobs actually pay

Here's the honest limitation first: only 11% of postings disclose pay. The median band across the 144 that do is $130,000–$167,663 a year. That's a real number from real postings, but it's a sample skewed by which companies disclose — US roles and regulated states are overrepresented in any transparency figure, so read it as "what disclosing employers pay," not "what the whole market pays."

Drilling into specific roles where I have enough disclosures to mean something:

• Data Scientist: $162,000–$200,000 (n=29)
• Data Engineer: $132,500–$166,825 (n=50)

The gap is the interesting part. Data scientists are commanding a band that starts roughly where engineers' band tops out. I wouldn't over-read 29 versus 50 disclosures, but the direction matches what I've seen elsewhere this year: the science/modelling premium is holding, and at the top end a senior DS posting that discloses is reaching $200k. Engineers cluster lower and tighter. If you're choosing which way to specialise and money is the deciding factor, the disclosed data points toward science — though the sample is small enough that I'd treat it as a nudge, not a mandate.

I keep the fuller pay breakdown, including how bands shift by level, updated at data salaries. Same disclosure caveat applies there: the 11% who name a number aren't a random sample of the 100%.

Who's hiring fastest right now

The fastest-hiring table this month is striking precisely because no single company dominates. The top of the list:

• Gen — +9 net, 9 active
• Checkout.com — +9 net, 9 active
• QED.ai — +8 net, 8 active
• MNTN — +6 net, 6 active
• Projekta Services — +6 net, 7 active
• Ebury — +5 net, 10 active
• Kikoff — +5 net, 5 active
• Lyft — +5 net, 15 active
• Plaid — +5 net, 5 active
• Waymo — +5 net, 9 active

Notice how tight the active counts are against the net numbers. For Gen, QED.ai, Kikoff and Plaid, the net adds essentially equal their entire active footprint in the dataset — meaning these roles all opened recently and almost nothing closed. That's a company turning on data hiring, not one steadily backfilling. Checkout.com and Ebury point to fintech and payments pulling hard; Lyft and Waymo cover mobility and autonomy. The leader board reads as fintech-plus-mobility with a security name (Gen) at the very top.

Lyft is the outlier worth flagging: 15 active roles but only +5 net, which means they've been opening and closing in roughly equal measure — a larger, more continuous hiring operation than the pure-net leaders. If you want a company that's reliably in-market rather than mid-spike, that profile matters.

Remote work: still the minority option

Remote roles make up 27% of postings. That number has been remarkably stable across recent editions, and I've stopped expecting it to move much. Roughly three in four data jobs still expect you on-site or hybrid in a specific location. If remote is non-negotiable for you, you're competing for about a quarter of the market — plan accordingly and don't assume the senior-heavy roles are the flexible ones. In my experience the platform and infrastructure work that anchors the engineering bucket often comes with location strings attached, because it sits close to security and on-prem systems.

What this means if you're job-hunting in June 2026

Pulling the threads together without pretending the data says more than it does:

The market is growing — a net +624 in 28 days with 382 first-time hirers is a healthy, broadening picture, not a contracting one. But it's growing for experienced people. Senior and principal roles outnumber junior ones by more than ten to one even before you count the unspecified pile, and almost all of that unspecified pile is mid-to-senior in disguise. If you're junior, your path is through the generically-titled roles and the new entrants, not through postings tagged "junior" — there aren't enough of those to build a search around.

Pick your lane between engineering and analytics knowing both are wanted in near-equal volume. The pay edge currently sits with the science side, but the engineering demand is just as deep, so the choice is about what you'd rather do, not where the jobs are.

Three concrete moves for the next four weeks:

• Hit the net-equals-active companies early. Gen, QED.ai, Kikoff and Plaid all show roles that opened recently with nothing closing — those reqs are fresh and the teams are actively building. Fresh postings get read; ones that have been live for two months often don't.
• Don't filter out untitled roles. The 1,865 unspecified-level postings are the largest single group. Skipping them because you can't tell the seniority means skipping the biggest chunk of the market.
• Treat the salary bands as a floor for negotiation, not a ceiling. With only 11% disclosing, the $130k–$168k overall median and the $162k–$200k data scientist band come from employers willing to name a number — typically the more competitive ones. If a non-disclosing company lowballs you well under these, you have real comparables to push back with.

And if remote is your hard requirement, go in eyes open: you're fishing in 27% of the pond, and that share hasn't budged in months. Better to target the companies that explicitly post remote than to filter a location-heavy market and come up empty.

I'll rerun all of this next month. The thing I'll be watching is whether that 382 first-time-hirer figure holds — if it does, the broadening of demand is structural, and that's good news for everyone except the people hoping a few big names will absorb the whole market.