Talent Pool Sizing: Where the Demand Is and Who's Driving It

Google alone accounts for 9.4% of all programme manager postings in our dataset. Capital One is the largest single hirer of ML engineers at 4.8% of demand. Meanwhile, the data engineer market is so fragmented that no employer holds more than 2.1% of postings across 1,213 distinct companies.

These concentration patterns matter. If you're a candidate, they tell you where the volume is and how much leverage you have. If you're an employer, they tell you who you're competing against and how differentiated your offer needs to be. We analysed 11,566 job postings across six role types to map the shape of demand.

The volume hierarchy

ML engineers lead the market by raw posting count at 2,946, followed by programme managers at 2,226, project managers at 2,003, data engineers at 1,997, and data scientists at 1,944. Analytics engineers sit well below the rest at 450 postings, a specialist role with a correspondingly smaller talent pool.

Management roles (project managers and programme managers combined) represent 37% of all tracked postings. That's a larger share than any single data role commands, and it often gets overlooked in conversations that focus on technical hiring.

Geographic hotspots

San Francisco dominates ML engineer hiring at 29% of postings for that role, and leads data scientist hiring at 24%. New York is the largest or second-largest market for every role in the dataset, with particular strength in data science (29%) and project management (25%). London holds a consistent second or third position across all roles, ranging from 15% to 32% of postings.

Denver stands out for management roles. It represents 19% of both project manager and programme manager postings, two to three times its share of data roles. This concentration suggests Denver is functioning as a regional management hub, with operational leadership clustered there while technical roles concentrate in the Bay Area and New York.

Singapore accounts for 2 to 8% of postings across roles, with a slight lean toward management positions. Remote-first postings vary from 2% for project managers to 11% for analytics engineers, suggesting that synchronous collaboration expectations remain strong for delivery roles.

Employer concentration: who controls the market

The most revealing pattern in this data is how differently each role's demand is distributed across employers.

Data engineers have the most fragmented market. The top 10 employers account for just 7.5% of all data engineer postings across 1,213 distinct companies. Capital One leads at 2.1%, followed by Meta, CVS Health, and Spotify at under 1% each. No single company has meaningful market power in data engineering hiring. For candidates, this means broad optionality. For employers, it means you're competing on compensation and culture more than brand recognition.

ML engineers show moderate concentration. Capital One again leads at 4.8%, with Waymo at 3.1% and Uber at 1.4%. The top 10 hold 16.5% of demand. The autonomous vehicle and fintech sectors are visible clusters, but 1,208 employers are still hiring for the role.

Programme managers are the most concentrated market in the dataset. Google holds 9.4% of all programme manager demand with 209 postings. The top 10 employers control 24.3% of the market. Oracle, Waymo, Amazon, JPMorgan Chase, and Meta round out the top tier. If you have programme management experience at a big tech company, your skills map directly to the largest demand pockets. But the concentration also means fewer distinct employers to negotiate between.

Hiring velocity: when the market moves

Tracking weekly posting volumes reveals clear seasonal patterns. Data roles peaked in mid-November (week 49) with 339, 260, and 352 postings for data engineers, data scientists, and ML engineers respectively. Activity dipped through the December holidays and rebounded in early January.

Management roles follow a different cycle. Project manager and programme manager postings barely registered before late December, then surged to 300 to 375 per week in January as new-year budgets activated. The hiring calendar for delivery roles is tied to organisational planning cycles, with a visible January pulse that data roles don't show as strongly.

ML engineers maintain the highest absolute weekly volume throughout the tracking period, consistently posting 200 or more openings per week outside of holiday dips. Analytics engineers are the smallest and most volatile, dropping to as few as 15 postings in a slow week.

The fragmentation paradox

The data engineer market has 1,213 employers and no dominant hirer. The programme manager market has 930 employers, with Google alone holding nearly 10%. Both are large markets. But the hiring experience in each is completely different.

In a fragmented market, brand doesn't matter as much. Candidates aren't comparing you against one or two dominant names. They're choosing between dozens of mid-size companies, and the decision often comes down to compensation, working arrangement, and what the team is building. In a concentrated market, candidates know exactly who the top hirers are. If you're not Google or Amazon for programme managers, you need a clear answer to why someone should choose you over the companies posting 200 roles a quarter.

The shape of the market matters more than its size. Knowing where you sit in that shape is the starting point for any hiring strategy that actually works.

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Based on 11,566 job postings from company career pages and aggregator sources, tracking data, product, and delivery roles across London, New York, Denver, San Francisco, and Singapore. Employer concentration analysis uses all sources. Geographic and velocity data covers November 2025 through February 2026. Full interactive dashboard at richjacobs.me/projects/hiring-market.