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Resume for a Data Analyst

The hardest thing about a data analyst resume is proving you did more than produce charts nobody acted on.

What a hiring manager scans for first

  • SQL, and how seriously you mean itAlmost every data analyst advert lists SQL, so listing it back proves nothing. What differentiates is context: the size of the tables, whether you wrote the models or just queried them, whether you tuned anything.
  • The tool the team already usesPower BI, Tableau and Looker are not interchangeable in a hiring manager's head. If the advert names one, it needs to be visible in the top third of your resume.
  • Evidence a decision changedThe strongest analyst bullets end with something a business did differently. A dashboard nobody opened is not an achievement.

The best resume format for this role

Reverse-chronological, single column, one page unless you have more than eight years. Analysts have an advantage here: your work produces numbers, so use them. Keep a short technical block near the top — SQL, the BI tool, the language, the warehouse — then let the experience section carry the evidence.

Skills worth listing

  • Querying and modelling. SQL first, and be specific about dialect if the advert is: Postgres, BigQuery, Snowflake, Redshift. Add dbt if you have used it — it is a strong signal.
  • Visualisation. The tool you would be comfortable building in on day one. One named well beats three named vaguely.
  • Programming. Python with pandas, or R. Only if you would use it in the job — plenty of good analyst roles never need it.
  • Statistics. A/B testing, regression, forecasting. Name what you have actually run, not what you studied.

Keywords that matter — and the honest way to use them

Analyst adverts lean heavily on the tool names and on verbs like "stakeholder", "self-serve", "data quality" and "reporting automation". Take them from the advert. Do not add a tool you have only watched a tutorial on — a technical screen will ask you to describe a join you wrote in it.

Never add a keyword for something you have not done. The term gets you past the filter and straight into an interview where someone asks you about it. Matching should change how you describe real work, not what work you claim.

Summary examples

Written to be read in four seconds. Notice that each one names a level, a context and one piece of evidence.

Commercial analyst

“Data analyst, four years in retail and e-commerce. I work mostly in SQL and Power BI, and I own the weekly trading pack that the commercial team runs its Monday meeting from. Cut its build time from a day and a half to under two hours.”

Product-facing

“Product analyst with three years supporting a subscription app. Built the churn model the growth team now prioritises against, and run the experiment readouts. Comfortable in SQL, dbt and Amplitude.”

Moving from finance

“Analyst moving from financial reporting into product analytics. Six years in Excel-heavy month-end work, two years of self-taught SQL and Power BI now used for the department's operational reporting.”

Experience bullets: before and after

The pattern is always the same — strong verb first, no first-person pronouns, and a number wherever one honestly exists.

Before

Responsible for building dashboards for the sales team.

After

Built the sales pipeline dashboard now used daily by 40 account managers; replaced a manual Excel pack that took two days a week.

Before

Analysed customer data to find insights.

After

Segmented 1.2m customers by repeat-purchase behaviour; the resulting targeting change lifted email revenue 14% in the following quarter.

Before

Helped improve data quality across the business.

After

Introduced dbt tests across 60 models, cutting failed overnight loads from roughly 8 a month to fewer than 1.

ATS tips specific to this role

  • Do not put a skills matrix with rating bars in it. It is usually a layout table, and parsers mangle it.
  • Spell out the tool and any common abbreviation once: "Power BI", "Business Intelligence (BI)". Filters match strings, not concepts.
  • Quantify the data, not just the outcome. "1.2m rows" tells a hiring manager more about your comfort level than "large dataset".
  • If your best work was a dashboard, say who used it and how often. Usage is the proof it mattered.

Templates that suit this role

  • Data Analyst — built for this role, though note it uses a sidebar
  • Finance Precision — single column and dense, good with lots of metrics
  • Single Column Plain — the safest choice for a large employer portal

Browse all 50 templates, or start from a blank one in the builder.

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Common questions

How long should a data analyst resume be?

One page if you have under about eight years of relevant experience, two if you genuinely have more to say. Length is not the point — a two-page resume where every line earns its place beats a padded single page.

Should I use a two-column template for a data analyst role?

You can, but understand the trade-off. Some parsers read multi-column layouts row by row and splice unrelated text together. If you are applying through a large employer's portal, a single-column layout is the safer choice; for a direct application to a small company it matters much less.

Do I need to rewrite my resume for every application?

Not the whole thing. Rewrite the summary and reorder your bullets so the most relevant ones sit at the top of each role. That is usually 15 minutes of work and it is the part that actually changes your odds.

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