Cohort 4Register

FOR RECRUITERS AND HIRING MANAGERS

Find the best people to interview, from up to 10 resumes.

Paste your job description and add your resumes. You get them ranked for that job, with the line of the resume behind each strength, the gaps, what to check, and questions to ask. You make the call.

Run it on your own resumes

A free account is enough. Reports stay private to your account.

Sign in to start

What you get

A sample report, made with made-up resumes

BEST TO INTERVIEW FOR DATA ENGINEER

  1. 1. Sample candidate B: 84 of 100
  2. 2. Sample candidate A: 61 of 100

A shortlist to help you decide. You make the call. The scores come from code. The written notes come from AI, so check them before you rely on them.

1. Sample candidate B

sample-candidate-b.pdf

84 of 100Interview first

Has run production Spark pipelines on AWS, which is the core of this job.

Strengths, with the line that shows each

  • Spark at scale. “Built PySpark jobs processing 2 TB a day on EMR”
  • Owns orchestration. “Moved 40 Airflow DAGs to a shared framework”

Gaps for this job

  • No streaming work shown, and the job mentions Kafka.

Worth checking

  • Ask for the size of the team on the Spark work.

Ask in the interview

  • A Spark job doubles in run time overnight. What do you check first?

Found: SQL, Python, PySpark, Airflow, AWS. Not found: Kafka.

2. Sample candidate A

sample-candidate-a.pdf

61 of 100Interview if time allows

Strong SQL and Python, with less proof on Spark and the cloud.

Strengths, with the line that shows each

  • Deep SQL. “Rewrote reporting queries and cut run time by 60%”

Gaps for this job

  • No PySpark or AWS work shown.

Ask in the interview

  • How would you move a daily SQL job to Spark, and when would you not?

Found: SQL, Python, Airflow. Not found: PySpark, AWS.

3. Sample candidate C

sample-candidate-c.pdf

38 of 100Not a match for this job

An analyst profile, with little pipeline work for this role.

Strengths, with the line that shows each

  • Business reporting. “Owned the weekly sales dashboard in Power BI”

Gaps for this job

  • No pipeline or orchestration work shown.

Found: SQL. Not found: Python, PySpark, Airflow, AWS.

Questions about resume screening

How does the ranking work?
Code scores each resume against your job description: must-have skills, keywords, sections and any hard requirement such as years of experience. It is the same scoring as our Resume Score. The written notes then explain each one for your job.
Can a candidate game it?
The report flags hidden white text, repeated keywords and words aimed at a screening tool, and tells you to check those by hand. Every strength it lists has to quote a real line of the resume, or we drop it.
Does it use a person's name, age, gender, religion or caste?
The score comes from code that does not use them. Before the AI writes its notes, we take the name, age, gender, religion, caste, marital status, email, phone and home address out of the text wherever we can find them, and we tell the AI never to use them. Resumes are free-form, so a detail can slip through. Your report still shows the name from the resume, so you know who is who.
What happens to the resumes?
We delete the files once they are read. The report deletes itself after 30 days, or sooner if you delete it. Nothing from them reaches our candidate board or any other tool. The text goes to our AI provider to write the notes.
What does it cost?
It is free with a sign-in, with a limit of 3 reports a day and 10 in any 30 days.
Which files does it take?
PDF, Word and text files, up to 2 MB each and 10 pages. A scanned PDF works, but a text PDF reads better.

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