Eleven thousand queries beyond position two hundred
A directory of engineers screened for technical and communication skills. US market, English-language Google, 10,436 tracked queries, and at the start exactly one of them was in the top 30. We split demand across stacks, roles and hiring tasks, rebuilt profile templates to assemble from data rather than from a name and a tech list, and added rate content – the top of the funnel that actually converts in this niche.
- Industry
- Software development, engineer directory
- Market
- US
- Period
- 12 months
- Metric
- Tracked queries in the top 30
- Starting point
- not disclosed
- Result
- 1,083
- Main work
- Stack and platform pages, portfolio turned into indexable pages, internal linking
Figures are taken from the client's own analytics for the period stated.
The curve is nearly flat for the first six months: that is indexation and structure that have not turned into positions yet. It takes off once the domain gains authority.
November 2022 against November 2023
The beyond-200 row is the important one: half the directory stopped being invisible.
Numbers from the client's own tools
Three reasons the directory wasn't ranking
Ten thousand profiles, almost none of them indexed
The directory held thousands of engineer and stack pages built from one template: a name, a list of technologies, a button. There was almost nothing distinctive on any of them, and Google kept eleven thousand queries out of eleven beyond position two hundred. The problem was not the page count but that every page was indistinguishable from the next.
Demand is worded through the stack, not through “hire a developer”
The generic hiring query competes with job boards, staffing agencies and listicles. The money sits in “hire a {stack} developer”, “{stack} developer for contract work”, “{role} for {task}” – lower volume, but the buyer arrives with a spec and a budget.
The buyer has been burned and is looking for proof of vetting
Anyone hiring a senior engineer remotely has almost certainly received a weak candidate from another platform before. A page that doesn't explain how the screening works – the stages, who reviews the code, what happens when a match fails – loses even from a better position.
Four stages over a year
Getting a grip on scale
An audit across ten thousand pages: what is indexed, what the crawler never reaches, where the duplicates are. We rebuilt the profile template so it assembles from real data – stack, years of experience, industries, screening results, hourly rate. Thin pages with no data were closed rather than left hanging.
Positions barely move. Only the indexed share does – from 12% to 74%.
Stack, role, task
We mapped 10,436 queries and split them across three axes: technology, role and hiring task. Every meaningful combination got a landing page with a real selection of engineers, a rate range and a description of who the directory screens in for that profile.
The first few hundred queries enter the top 30. The top 10 is still empty – the domain lacks authority.
Answering “do you really vet them”
We documented the screening process step by step: sourcing, technical interview, communication check, trial period. Added a section on rates by stack and market – the most-visited top-of-funnel content in this niche. Every page was reviewed by the client's CTO.
218 queries in the top 10, 106 in the top 5. Enquiries start arriving from rate queries, not only from direct hiring ones.
Industry publications
Outreach to publications on remote engineering, HR tech and engineering management, plus contractor directories. Links pointed at cluster pages rather than the homepage, so the authority landed where it actually moved something.
1,083 queries in the top 30 and 24 at position one. 5,471 queries climbed out of the beyond-200 bucket.
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