Data Jobs Hiring Report — June 2026

Key facts

• 4,316 data roles were open across 1,970 companies on direct-apply Greenhouse, Lever and Ashby boards as of June 2026.
• Net +670 roles in the last 28 days — 2,181 opened against 1,511 closed — so hiring is clearly expanding, not just churning.
• 458 companies posted their first data role of the tracking period in the last 28 days, a wide-base signal rather than a few giants scaling.
• Median disclosed band sits at $129,500–$165,913/yr, but only 12% of postings (n=148) name a number.
• Data Scientist postings that disclose pay run $162,000–$200,000 (n=29) — well above Data Engineer at $135,000–$166,825 (n=50).

The headline I'd lead with isn't the 4,316 open roles — it's that 458 companies opened their first data role in the last four weeks. That's nearly a quarter of all employers currently hiring showing up fresh. When net new roles run +670 and the new-company count is that high, you're not looking at a handful of well-funded names hoovering up headcount. You're looking at demand spread thin across a long tail.

How many data jobs are open right now?

There are 4,316 open data roles across 1,970 companies in the dataset I track, which pulls daily from companies' own Greenhouse, Lever and Ashby boards — direct-apply postings, no aggregator noise, no reposted ghost listings from job boards. That works out to roughly 2.2 open roles per company, which tells you most employers are filling one or two specific gaps rather than building teams from scratch.

You can browse the live set on /data-jobs. The number itself matters less than the flow behind it, which is where the next section comes in.

Is data hiring growing or cooling?

Growing, and the math is unambiguous: 2,181 roles opened in the last 28 days against 1,511 closed, for a net gain of +670. A closed role isn't always a filled one — some get pulled or restructured — but a 1.44-to-1 open-to-close ratio is a healthy expansion signal in any reading.

What I find more useful than the net figure is the churn underneath it. Over 1,500 roles disappeared from boards in four weeks. That's normal turnover, and it's a reminder that the market moves fast in both directions: a posting you bookmark today may be gone in three weeks whether it got filled or not. Apply early. The companies tracking their own pipelines on /movers/data show exactly how quickly individual employers ramp up and wind down.

Which roles and functions dominate?

Engineering and analytics split the market almost down the middle: 2,252 engineering roles and 1,946 in data analytics, with everything else — product, operations, sales — barely registering in single or double digits. So when someone says "data jobs," 97% of what's actually being advertised is one of two things: building the pipelines and platforms, or analysing what flows through them.

That balance is worth sitting with. Analytics roles being nearly level with engineering is a shift from the picture a couple of years back, when engineering ran well ahead. If you read SQL, build dashboards and can own a metric end to end, there's genuine volume here — you're not competing for scraps behind the platform engineers.

What seniority levels are companies hiring for?

Senior is the dominant specified level, with 1,741 roles, followed by principal at 431. The largest single bucket, though, is "unspecified" at 1,898 — meaning the posting didn't pin a level, which is common for analytics and generalist data jobs where the title carries the signal instead.

The brutal number for newcomers: just 130 junior roles, against 1,741 explicitly senior ones. Director (99), executive (14) and VP (3) round out the top, so leadership openings are rare too — most of this market is mid-to-senior individual contributors. If you're early-career, that 130 figure is the reality check. The roles exist, but they're roughly one in thirty-three, and they go fast. Treat the "unspecified" pile as your second hunting ground — some of those are open to less experience than a "Senior" title would imply.

What do data jobs pay?

The median disclosed band is $129,500–$165,913/yr, but I'd attach a heavy asterisk: only 12% of postings disclose pay at all (n=148). Eighty-eight percent of employers in this dataset still publish a role without a number. So the figure is real, but it's drawn from the minority of companies that show their hand — disproportionately those covered by pay-transparency rules or competing hard enough to lead with comp.

Break it out by role and the spread sharpens. Data Scientist postings run $162,000–$200,000 (n=29) — the clear premium tier. Data Engineer sits at $135,000–$166,825 (n=50), which lines up almost exactly with the overall median, unsurprising given how much of the engineering volume is data engineering. The gap between the two is roughly $25–35k at the midpoint, a meaningful premium for the science track if your skills point that way.

Don't over-read sample sizes of 29 and 50; these are directional, not gospel. But the direction is consistent with what I've seen edition over edition. Full breakdowns and history live at /data-jobs/salaries. And if you're negotiating against a posting with no band, these medians are a reasonable anchor to walk in with.

Which companies are hiring fastest?

No single company is dominating, which is itself the story. The fastest-growing board over 28 days is a tie: Checkout.com and Gen Digital each added a net +9 roles. Both currently sit at exactly 9 active data roles, meaning essentially their entire data footprint here is brand new this month — a from-near-zero ramp rather than steady accumulation.

Coupang is the most substantial name on the leaderboard: +8 net but on a base of 24 active roles, by some distance the largest standing data team in the top ten. When a company that already carries two dozen open roles keeps adding, that's a sustained build, not a one-off burst. QED.ai also added +8, all 8 of its active roles new.

The rest of the pack clusters tightly: MNTN (+6, 6 active), Waymo (+6 net on 10 active), Lyft (+5 on 16 active), Kikoff (+5/5), Hostinger (+5/5) and Grvty (+5 on 15 active). Notice how many — MNTN, Kikoff, Hostinger — show net new equal to total active. Those are fresh entrants spinning up data hiring from scratch this month. Waymo, Lyft and Grvty, by contrast, are growing on top of existing teams.

The practical read for a job seeker: the from-zero names (Checkout.com, Gen Digital, QED.ai, Kikoff, Hostinger, MNTN) are where you're applying into a team that's being defined right now — more ambiguity, potentially more influence over how the role is shaped. The established-base names (Coupang, Lyft, Grvty, Waymo) offer a clearer existing structure to slot into. Neither is better; it's a question of what stage you want to walk into. Track week-to-week movement at /movers/data.

Can I work remotely?

28% of data roles are remote — a bit over a quarter. So remote is a real option but not the default; nearly three in four postings expect you in a location, at least part of the time. That share has held fairly steady, and it's roughly in line with the broader tech market rather than ahead of it. If remote is non-negotiable for you, plan to filter aggressively and accept a meaningfully smaller pool — call it around 1,200 of the 4,316 open roles.

Is this a good time to look for a data job?

For mid-to-senior engineers and analysts, yes — the conditions are favourable. A net +670 over four weeks, 458 new employers entering, and a near-even split between engineering and analytics work means demand is both growing and broad. You're not betting on one hot sub-field; both major tracks have thousands of live roles.

For juniors, it's harder, and I won't sugar-coat 130 explicit junior roles. The path in runs through the "unspecified" pile and through smaller companies — the from-zero hirers on the leaderboard tend to be less rigid about exact years than a large org with a banded ladder. Cast wider, apply faster, and lean on demonstrable project work since you can't lean on a senior title.

One honest caveat on the whole picture: this is first-party board data, so it captures companies that hire through Greenhouse, Lever and Ashby — strong coverage of tech-forward employers, lighter on enterprises using older systems. And the pay figures rest on the 12% who disclose. I'd rather tell you that than dress up the numbers as the whole market.

The takeaway for job seekers

If you're senior in engineering or analytics, this is a buyer-friendly month — go after the established-base names like Coupang (24 active) and Lyft (16) for structure, or the from-zero ramps like Checkout.com and Gen Digital if you want to help define a role. Anchor your comp expectations at the $129.5k–$166k median, and push toward $162k–$200k if you're on the data science track and the company discloses nothing. Apply within days of a posting going live, not weeks — 1,511 roles closed in the last month, and not all of them got filled. If you're junior, treat the "unspecified" roles and the brand-new small hirers as your real target list, because the 130 labelled junior openings won't carry you alone.