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Stop choosing between applying fast and applying well.

Send the same application to fifty jobs and hope. Or spend your evenings finding the right three and tailoring each one. SwiftFit does the second at the speed of the first.

No. AR-2026-08-4471Issued Aug 17, 2026, 09:04 AM

Selection readout

Illustrative: authored for this page.

2 above cut-off
Source
Company boards
Screened
5,412
Assayed
30
Cut-off
65
Illustrative postings with their assay grade against a cut-off of 65

Staff Data Engineer, Platform

Verrill Grid

9191 out of 100, above the cut-off of 65, fire-assayed against your profile

Senior Engineer, Data Infrastructure

Halden Systems

8484 out of 100, above the cut-off of 65, fire-assayed against your profile

Analytics Engineer

Motte & Co. · Two levels below your seniority.

6464 out of 100, below the cut-off of 65, fire-assayed against your profile

Backend Engineer, Ingestion

Pelham Labs

6363 out of 100, below the cut-off of 65, screened by similarity only

You have been picking one or the other.

Both work. Each costs you the thing the other one gives.

How the two usual approaches compare with SwiftFit
Generic, at scaleTailored, by handSwiftFit
Jobs that actually fitHit and missYesYes
An application written for the jobNoYesYes
Evenings it costs youFewMost of themFew

You see the jobs worth your time, read the resume already written for the one you pick, and apply.

Every grade shows its working.

Four elements, scored separately. The weakest one explains the verdict.

Sample GH-40812 · fire assay

Staff Data Engineer, Platform · Verrill Grid

Work
9/10
Industry
8/10
Level
9/10
Day to day
7/10

Pipelines and warehouse modelling. Energy data, like your last two roles. Staff, with a scope statement. On-call you did not ask for.

Reported grade

91

Above the cut-off of 65.

Verdict

Apply

Weakest element

Day to day, at 7.

Then it writes the one you send.

Rewritten for that posting, from your own resume. Never invented.

Before
  • Built and maintained ETL pipelines for the analytics team, moving data from production Postgres into the warehouse nightly.
  • Worked with stakeholders across the business to define reporting requirements and deliver dashboards.
After
  • Owned the nightly ingestion path from production Postgres into the warehouse, the ETL and the dimensional models on top of it.
  • Set reporting scope directly with the teams that used it, then built and maintained the dashboards against those definitions.

Same jobs, same dates, same numbers, written to answer this particular posting.

Start with either.

Five steps to a ranked list. Or none at all to a better resume.

The resume you build in the editor comes with you if you sign up later.