SourceLens for IT Recruiters: Tech-stack context for every candidate

If you recruit engineers, you already know the problem.

You open LinkedIn for a Senior Backend Engineer role at a Series B fintech. The search returns 240 profiles. About 30 of them work at companies you recognise. The other 210 are SaaS startups, infra companies and tooling vendors you have never seen before.

Try it on your own role. Install SourceLens free for 14 days and run one of your live tech searches with employer context next to every profile.

“Senior Engineer at Lumera Labs” tells you nothing. Are they pre-seed or Series C? Go and Kubernetes, or PHP monolith? Two engineers, or a 200-person tech org? Without that context the title is meaningless, but with 210 unknown employers in the list you cannot Google all of them. So you skip the ones you do not recognise and you fall back to logos.

That is the structural gap SourceLens for IT recruiters is built to close.

Why tech is the hardest segment for employer-blind sourcing

For some roles the title carries most of the signal. A Java developer is a Java developer. A staff accountant is a staff accountant. The function does the work.

Tech does not behave like that.

A React developer at a fintech building a regulated B2B platform writes very different code than a React developer at a B2C marketplace shipping consumer features weekly. A platform engineer at a 12-person seed startup makes different tradeoffs than a platform engineer in a 200-person infrastructure org with dedicated SRE teams. The tech stack matters. The company stage matters. The customer segment matters.

LinkedIn shows you the title and the employer name. It does not show you the rest.

The 18 dimensions, and which ones matter most for tech

SourceLens scores every employer on 18 dimensions. For a tech-recruitment workflow the dimensions that earn their keep are:

• Funding stage: pre-seed, seed, Series A through D, PE-backed, public. This is the strongest single predictor of team size, engineering practice and compensation band.
• Tech-stack signals: languages, infrastructure, observable from product descriptions, hiring posts and engineering content. Not a substitute for a deep technical review, but enough to decide “worth a closer look” or “wrong stack”.
• Company size and engineering-org maturity: a 12-person startup is a different job from a 1,200-person tech org. Both can be valid for the role, but rarely the same role.
• Growth stage: early product, scaling, mature platform. Maps onto what kind of engineering work the candidate actually shipped.
• B2B versus B2C versus B2B2C and industry vertical: regulated fintech is not consumer social. Healthtech is not gaming. Vertical decides what the engineer has actually built.
• Product complexity: API, platform, consumer app, infrastructure, data. The shape of the product shapes the engineer.

These are surfaced next to every profile, automatically, without you leaving LinkedIn.

What changes in the workflow

The old workflow: open 240 profiles, recognise 30, do shallow review on those, skip or guess on the rest. Average screening time per role: half a day. Average shortlist quality: heavily biased toward known logos.

The new workflow: scan the same 240 profiles with employer context already visible. Spend three seconds per profile deciding fit. Build a shortlist that includes the unknown 8-person startup running exactly your stack. Walk into the hiring manager call knowing why each candidate is on the list.

Sourcing time per role drops from roughly three hours to roughly 30 minutes. The freed-up time goes back into InMails, intake calls and closes. These are the parts of the job that move requisitions.

The hidden-engineer problem

A second pattern matters for tech specifically.

Strong engineers often leave their LinkedIn description blank. Just a title and a current employer. No summary, no bullet points, no buzzword soup. LinkedIn Recruiter ranks these profiles low because there is no text to match against the search string. So the platform hides them at the bottom of the result list, and recruiters who scroll only the first two pages never see them.

SourceLens reads the employer instead of the profile. A bare profile at the right Series B SaaS company still surfaces with the right context attached. These engineers end up on your shortlist while other recruiters miss them entirely.

Pricing for tech recruiters

The Starter plan is €89 per month with launch code LINKEDIN25, valid through 30 April. After that the price normalises but early adopters keep the launch rate.

A tech-recruiter fee is normally €15k to €25k per placement. One extra placement per quarter from engineers you would otherwise have skipped pays the plan back many times over. The math is not subtle.

The trial is 14 days, no credit card required. The Chrome extension installs in under a minute and works on any LinkedIn tier, including the free Basic account.

Where this fits

SourceLens is not a replacement for technical evaluation. It does not read GitHub. It does not score code. It replaces the per-employer research step that eats half of a typical sourcing day.

If you spend two evenings a week in Crunchbase, StackShare and company About pages trying to decode what unknown SaaS startups actually do, SourceLens is built for you. If your tech roles already source themselves because the candidates queue up at the door, you can probably skip it.

For everyone in between, the tech recruiters and engineering recruiters quietly fighting employer-blindness every day, the IT recruiter landing page lays out the full picture, and the free trial does the rest.