Data Analyst CV Example & Writing Guide

The full CV example

Track how often this example ends a bullet with a decision or a behaviour change rather than with a deliverable. That habit is the whole difference between a reporting CV and an analyst CV.

Fictional example. Every name, employer and figure here is invented; use your own.

Why this CV works

Five things this example does that a dashboard-list CV cannot:

• The lead bullet is a definition error, not a deliverable

  Margin calculated before rebates in one of two paths, three quarters of overstated performance, a changed buying decision. Metric definitions are where analysts create and destroy the most value, and almost nobody puts one on a CV because it feels like admitting something was broken.

• It counts dashboards removed

  22 of 31 retired after checking which were actually opened. Anyone can build dashboards. Deleting the ones nobody uses, and being able to say how you decided, is a judgement signal that survives any tooling change.

• Query tuning is tied to what it enabled

  11 minutes to 40 seconds is an engineering number. "Which is what made self-serve filtering usable for 40 store managers" converts it into a business one, and shows the analyst understood why the speed mattered.

• It includes an analysis someone argued with

  The bakery readout that regional managers used to push back in three stores. An analyst willing to write that down is telling a hiring manager they publish findings that can be challenged, which is the only kind worth having.

• The finance job is the origin story

  One line: supplier reconciliation in Excel, rebuilt as a database. Career changers into analytics are common, and the earlier role is an asset when it explains domain knowledge rather than sitting there unexplained.

Professional summary examples

Entry level

Early on, one dashboard that changed something beats a list of four tools. Naming what you are still learning is credible rather than weak.

Mid level

Mid-level should carry one finding and one piece of infrastructure. The last line states the standard you hold your own work to.

Senior

Senior analysts are hired to settle definitions and to be trusted by people who do not know SQL. Describe the disorder you are good at resolving.

Experience bullet examples

Twelve bullets in weak and strong form. Every figure belongs to the fictional example - yours must come from your own queries and your own stakeholders, because the obvious interview question is how you calculated it and who acted on it.

Weak

Created dashboards for the business.

Stronger

Rebuilt the Monday trading report as 14 tested dbt models feeding one dashboard, cutting 6 hours of manual assembly to a 20-minute refresh ready before the 08:00 meeting.

Why

The deadline is what makes the time saving a result. A faster report that lands after the meeting has changed nothing.

Weak

Analysed sales data.

Stronger

Found that promotional margin was calculated before supplier rebates in one of two reporting paths, overstating promotion performance by roughly 8%; the corrected definition changed the next promotion cycle.

Why

A definition error with a consequence is the strongest bullet a data analyst can write, and the most commonly omitted.

Weak

Wrote SQL queries.

Stronger

Cut the store performance query from 11 minutes to 40 seconds by materialising the daily sales aggregate, which made self-serve filtering usable for 40 store managers.

Why

Query performance is only interesting when it changed who could use the data.

Weak

Maintained reports and dashboards.

Stronger

Retired 22 of 31 dashboards after logging which were opened in 90 days, then rewrote the remaining 9 around the four questions the commercial team actually asked.

Why

Say how you decided what to cut. The method is the credibility.

Weak

Worked with stakeholders to understand requirements.

Stronger

Trained 18 category managers to answer their recurring questions in the dashboard, cutting ad hoc analyst requests from about 25 a week to 9.

Why

Self-serve claims need a before and after request count, or they are a slide from a strategy deck.

Weak

Improved data quality.

Stronger

Reconciled member counts across three systems that disagreed by up to 4,000 records, and documented the joining rules so the reconciliation stopped being rebuilt each quarter.

Why

The documentation is the part that stops the work recurring. Include it.

Weak

Used Python for data analysis.

Stronger

Automated the monthly regulator submission pack in SQL and Python, removing two days of spreadsheet copying and the transcription errors that came with it.

Why

Name the error class that disappeared, not only the hours.

Weak

Presented findings to management.

Stronger

Wrote the two-page basket analysis readout the regional managers used to argue against a shelf reallocation in three stores where the data did not hold.

Why

Analysis that survived disagreement is more persuasive than analysis that was applauded.

Weak

Supported A/B testing.

Stronger

Ran the readout on a delivery-fee test, recommending against the rollout because the revenue gain sat inside the confidence interval while basket size fell in two regions.

Why

A recommendation against something is evidence you read results rather than reported them.

Weak

Built data models.

Stronger

Moved 14 reporting queries into version-controlled dbt models with tests on row counts and null keys, which caught a broken upstream feed before the trading meeting twice in the first quarter.

Why

Tests are worth the space when you say what they caught.

Weak

Worked with the finance team.

Stronger

Reconciled the analytics revenue figure with the finance ledger and documented the three legitimate differences, which ended a recurring monthly dispute about which number was correct.

Why

Two teams quoting different numbers is a standard organisational problem. Solving one is a specific, checkable claim.

Weak

Improved reporting processes.

Stronger

Replaced eight emailed spreadsheet extracts with a single scheduled report, after establishing that six of the eight recipients only ever read one column.

Why

The investigation behind the change is what distinguishes this from tidying up.

Skills that belong on this CV

Education and certifications

Statistics, economics, mathematics and engineering degrees are the common routes, and analytics also takes people from finance, operations and science backgrounds. Keep education to degree, institution and years below experience once you have two analyst roles. If you converted from another function, show that role in one line: domain knowledge of the business you analyse is an advantage, and hiding it costs you the explanation.

Certifications

• Microsoft PL-300 (Power BI Data Analyst) - Worth listing when the advert names Power BI, which many corporate reporting roles do.
• Google Data Analytics Professional Certificate - Useful for a first analyst role as evidence of deliberate study, and quickly outweighed by one piece of real work.
• dbt Analytics Engineering Certification - Relevant for roles that sit between analysis and the warehouse, where the advert mentions models rather than reports.

No certificate is required to work as a data analyst. A short public notebook or query walkthrough, showing how you checked your own numbers, persuades more than any of them.

How an ATS reads this CV

Appliora's ATS Checker runs 14 checks against an uploaded file and shows the text its parser extracted. Four of them catch problems that analyst CVs run into more than most, usually caused by an instinct to visualise everything.

• Concrete skills

  Analyst skills blocks fill with tool names because the tools are the vocabulary of the field, and a list of thirty scores worse than a list of twelve. The check looks for recognisable skills rather than fragments. Group them - languages, warehouse, visualisation - and drop anything you would not want to be tested on in a live SQL screen.

• Bullet quality

  This is the heaviest content check: how many bullets carry a figure and how many open with an action verb. Analyst CVs fail it in a particular way, by being full of nouns - "responsible for weekly reporting and ad hoc analysis" - where the numbers describe the work's frequency rather than its outcome.

• Format traps

  Miniature charts, a screenshot of a dashboard and skill rating bars all appear on analyst CVs and all are invisible to a parser. If a visual is central to your case, put the portfolio link in text and let the page carry the picture.

• How it reads

  The check reads for clarity and repetition. Analyst CVs repeat the same three verbs - analysed, created, supported - down every role, and the repetition reads as a single job described three times. Vary the verb because the action was genuinely different, not to satisfy a checker.

Templates that suit this role

• ATS Structured

  One column and conventional headings, which keeps a tool list and a dense experience section in a predictable order for corporate and public-sector portals.

• ATS Clean

  Enough room for a certifications line and a grouped skills block without a sidebar, which suits reporting roles where the tool match is screened first.

• Two Column Pro

  For direct applications where you want tools and certifications in a side panel while the experience carries the findings and the decisions they changed.

Related roles

• Data Scientist
• Business Analyst
• Product Manager
• Financial Analyst