Hire a Data Engineer, ranked by real evidence.
Plaza matches you with Data Engineer candidates — pipelines and warehouses that stay correct at scale — whose skill is proven by measured public GitHub work — repository languages counted in bytes, push dates and stars, not a polished resume. Every candidate arrives with a Trust Score, the repositories the evidence came from, and a gap analysis against your role.
Evidence, not applications.
Verified skill
A Trust Score from real Data Engineer activity and ownership evidence — you see the proof, not just claims.
Gap analysis
Every match is scored against your role, with a clear note on where a candidate is strong and where a gap remains.
Nothing to spam
Plaza publishes no contact details: reaching someone takes a request they can decline, and only one they accept opens a conversation. Nobody's inbox is sold, flooded or exposed.
Before you hire.
How does Plaza verify a Data Engineer's skills?
Plaza builds a Trust Score for every Data Engineer candidate from measured public GitHub work — repository languages counted in bytes, push dates and stars — measured from their public repositories, not read off a self-declared resume. Each match shows the repositories the evidence came from, the arithmetic that ranked it, and a gap analysis naming the requirements it found no evidence for.
Is Plaza a Data Engineer job board?
Plaza is a matching layer, not a job board. Instead of a wall of applicants you get Data Engineer candidates ranked by measured evidence — 0.85 × skill-evidence coverage + 0.15 × trust score — each with a human-readable reason and the repositories behind it, and a candidate with no measured evidence for a requirement is never returned.
Can I contact a candidate through Plaza?
Plaza publishes no contact details, so nothing here can flood an inbox: reaching a builder takes a structured request that states why you are getting in touch, nothing further reaches them unless they accept it, and there is a cap on how many anyone can send in a day.
What does Plaza actually measure about a Data Engineer?
Only public GitHub evidence, read through the REST API: languages counted in bytes, when each repo was last pushed, repos owned, followers and stars. Nothing is self-reported and no model invents a number — these are constants in the code.
- Skill strength
- 50% recency · 30% measured volume · 20% breadthThe three terms behind one skill's strength (app/lib/match.ts).
- Self-declared topics
- kept at 60% strengthA repo topic the owner typed is weaker evidence than bytes we counted, so it is discounted rather than trusted.
- Final ranking
- 85% skill coverage + 15% Trust ScoreCoverage dominates on purpose: the question is whether they can do this, not whether they are famous.
- Trust Score, out of 100
- code depth 28% · repos owned 18% · recency 15% · cadence 12% · account age 12% · stars 8% · followers 7%Seven components (app/lib/github-evidence.ts). Followers and stars — the two numbers you can accumulate without writing code — are worth 15% together.
- Recency half-life
- 60 daysNot a yes/no 'active' badge: someone who pushed yesterday reads differently from someone who pushed on day 89.
- Cadence window
- 90 days, saturating at 30 reposHow much ground an account covered recently, counted from push dates.
- Repos-owned band
- 1 repo to 100 reposLog-scaled between a floor that says nothing and a knee only the top of the real population reaches.
- Account-age band
- 6 months to 15 yearsMeasured from the GitHub account's own creation date, never self-reported.
- Language bytes read
- the 8 repos pushed most recentlyGitHub allows 60 unauthenticated REST requests in 1 hour, so byte-level detail is spent on the freshest work first.
- Cost to search
- US$0, no accountSigning in with GitHub is also free; it indexes your own public activity so others can find you.
- Contact limit
- 5 outgoing requests per 24 hoursA per-person cap, so nobody can be volume-mailed through Plaza.
- Sign-in session
- 30 daysA signed cookie, refreshed on sign-in. Plaza stores no password of yours at all.
How do you check that a developer really wrote what they claim?
Five things people actually do, and what each one can and cannot establish. Reading a CV proves nothing checkable; an interview measures interview performance; public code proves authorship but only of public code.
| Method | What it actually proves | Typical time | Cost | Where it fails |
|---|---|---|---|---|
| A CV or résumé | Nothing checkable — every line is written by the candidate | 1–2 minutes to read | US$0 | No claim on it points at an artifact you can open |
| Reading their GitHub by hand | Authorship of public repos, if you open each one | 10–20 minutes per person | US$0 | Does not scale past a shortlist; forks and starred repos read like their own work |
| A technical interview | How they solve one fresh problem while watched | 45–60 minutes, two people | 1–2 hours of engineering time | Measures interview performance, not what they have shipped |
| A take-home exercise | New code they wrote for your prompt | 4–8 hours of theirs, 30 minutes of yours | Unpaid work, or a real fee | High dropout, and it still says nothing about their past work |
| Plaza's measurement | Bytes of each language in their public repos, push dates and stars — with every repo cited | Under 1 minute | US$0 | Blind to private and company-internal work, which is most work |
Only the last row is measured by Plaza. The times and costs on the other four are typical industry practice, stated as ranges — we did not measure them, and the table says so rather than dressing an estimate up as a finding.
Where can you verify all of this yourself?
Every input Plaza reads is a documented, public GitHub endpoint, and every figure above is a constant in open code. These are the first-party specs — not commentary about them.
- Apache Spark — official docs — the Apache Software Foundation's reference
- GitHub REST API — Repositories — the endpoint every repo, star count and push date on Plaza is read from
- GitHub REST API — Users — account age, public repo count and followers, the three account-level trust inputs
- GitHub REST API — rate limits — why an ingest reads language bytes for 8 repos and not for all of them
- GitHub's own OpenAPI description — the machine-readable contract for the responses above, published by GitHub
- api.github.com/rate_limit — live — open it and GitHub itself returns the 60-per-hour figure quoted above
- git log — Git reference manual — the same authorship record, readable offline once you have cloned the repo
- Open Source Guides — how to contribute — what public contribution history does and does not say about a person
Hire by role.
Find your next Data Engineer.
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