How to Screen a Data Analyst Resume: Must-Haves, Red Flags and a Scoring Rubric
Data analyst CVs are full of tools. Screen instead for evidence that the candidate turned a question into an answer somebody acted on, and that they know when a number is wrong. Those two things predict the job; the tool list predicts the tool list.
- Read for evidence before matching keywords: a data analyst CV should show what the candidate built, ran, closed or decided, with a result. The must-haves below are the four things the job cannot be done without.
- Must-haves: sQL in daily use, evidenced by the work described, not only the skills section; at least one analysis with a decision attached: what was asked, what was found, what changed. Nice-to-haves raise a candidate; they do not rescue one missing a must-have.
- Red flags are patterns, not single lines: every bullet is a tool ('built dashboards in Tableau') with no question answered, and 'Insights' with no decision or outcome attached.
- Use the five-criterion rubric on the page, weighted so that decisions influenced counts more than job description match. It is written to be pasted into WadeCV as a role and scored automatically.
- Spend three minutes per CV with the rubric and write one line of evidence per criterion. Screening decisions with evidence attached can be explained to the candidate, to the hiring manager, and to anyone who audits them later.
Must-haves
- SQL in daily use, evidenced by the work described, not only the skills section
- At least one analysis with a decision attached: what was asked, what was found, what changed
- Experience with the kind of data the role has (product events, financial, operational, marketing)
- Some form of communication to non-analysts: dashboards, decks, memos, with an audience named
Nice to have
- A scripting language for analysis (Python or R) used for something specific
- Experiment design or statistical testing done, not listed
- Data modelling or pipeline work in a warehouse
- Domain knowledge for your industry
Red flags
- Every bullet is a tool ('built dashboards in Tableau') with no question answered
- 'Insights' with no decision or outcome attached
- A statistics vocabulary the work history does not use
- No stakeholder ever named
The rubric
Score each criterion from what the CV shows; weight 3 counts three times weight 1. In WadeCV, create the role with these criteria and every CV is scored this way automatically.
| Criterion | How to measure it | Weight |
|---|---|---|
| Job description match | Match between the candidate's data domain, tools and the role as written | 2 |
| Years of relevant experience | Years doing analysis for decision-makers, not total years in data roles | 2 |
| Decisions influenced | Analyses with a named question, finding and consequence | 3 |
| SQL and technical depth | Evidence of non-trivial SQL and of scripting or modelling where relevant | 2 |
| Communication | Evidence of presenting findings to non-analysts, with the audience named | 2 |
How to read the CV
Look for the sentence 'which led to'. Analysts who influenced decisions write it, sometimes clumsily, because it was the point of the work. Analysts who produced reports write about the reports.
Then check technical depth in the work history, not the skills section. 'Wrote the SQL model for weekly cohort retention used by the exec dashboard' is depth. 'SQL (advanced)' is a claim.
Do not weight tool lists. A good analyst will pick up your BI tool in a fortnight. Weight the decisions influenced and the communication, because those are what your stakeholders will experience.
Frequently asked questions
Should we require a degree in a quantitative subject?
No. Require evidence of quantitative work. Plenty of strong analysts came from economics, geography or a career change, and the rubric will find them.
How do we screen junior candidates with no work history?
Projects with a question and an answer count. A dissertation, a volunteer analysis, a personal project on public data with a conclusion they can defend. Score decisions influenced on those.
Is a portfolio expected?
Not expected, but a public notebook or dashboard with a clear question and a written conclusion is the strongest evidence a candidate can offer at screening. Read it if they have one.
Related guides
- How to Screen a Product Manager Resume: Must-Haves, Red Flags and a Scoring Rubric
- How to Screen a Marketing Manager Resume: Must-Haves, Red Flags and a Scoring Rubric
- How to Screen a Backend Engineer Resume: Must-Haves, Red Flags and a Scoring Rubric
- How to Screen a DevOps Engineer Resume: Must-Haves, Red Flags and a Scoring Rubric
Screening data analyst CVs this week?
Paste the job description into WadeCV, check the rubric it drafts, and upload the CVs. Each one is read against the rubric and scored with an explanation. Ten screenings are free; there is no subscription.
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