Data and analytics · Mid level · UK and US conventions noted

Data analyst: resume example, explained

This fictional example shows a data analyst working in e-commerce reporting since 2022 and applying for a mid-level analyst role: every bullet names the question asked, the data used and the decision that followed, with SQL, Power BI and experiment readouts shown in use rather than listed. Each decision is explained, four weak bullets are rewritten from the evidence behind them, and the editable download lets you replace every detail with your own. Nothing here describes a real person.

Illustrative example. An illustrative, invented analyst working in a fictional online retailer's reporting team since 2022, applying for a data analyst role at a fictional subscription software company. Every employer, dataset, date and figure is fictional.

Editable version of this example (.docx)

Priya Venkataraman

Data Analyst · SQL, dashboards and experiment readouts · E-commerce analytics since 2022

Manchester, UK (open to hybrid) · 07700 900 456 · priya.venkataraman@example.com

Profile

Data analyst in a fictional online retailer's reporting team since 2022, working mostly in SQL on an orders warehouse of around 40 million rows. Turns stakeholder questions into scoped analyses, maintains 12 Power BI dashboards used weekly by trading and finance, and owns the metric definitions that stopped two teams reporting different revenue figures. Seeking more experiment design and closer work with product teams.

Experience

Data Analyst, Trading and Finance Reporting — Fictional Homeware Online Ltd

March 2024 – present · Fictional online homeware retailer; reporting team of four analysts supporting trading, finance and marketing.

Turn around 25 stakeholder questions a month into scoped analyses, writing up the question, the data, the answer and its limits before any chart is built.
Maintain 12 Power BI dashboards refreshed daily, including the weekly trading pack the commercial director uses every Monday to set that week's promotions.
Wrote shared SQL definitions for net revenue, returns rate and active customer, ending a six-month disagreement between the finance and marketing reports.
Investigated a 9 per cent drop in reported conversion, traced it to a duplicated checkout event on the Android app and corrected three months of board figures before the quarterly review.
Produced readouts for eight A/B tests on checkout and delivery pricing, stating confidence intervals and the smallest detectable effect, which stopped one underpowered test being read as a win.

Junior Reporting Analyst — Fictional Homeware Online Ltd

September 2022 – March 2024

Rebuilt the weekly stock-availability report from a manual Excel workbook into a scheduled SQL query and dashboard, cutting preparation from about four hours to 20 minutes.
Reconciled web analytics sessions against warehouse orders each month and logged every mismatch above 2 per cent for the engineering team.

Education

BSc (Hons) Economics, Fictional University, 2022
Online course in experiment design and applied statistics, completed 2024

Selected analyses

Delivery pricing test readout (2025): recommended keeping the free-delivery threshold; adopted by trading.
Returns review (2024): most of the rise sat in two furniture ranges, which informed a packaging change.

Skills

SQL (window functions, CTEs, query tuning on a columnar warehouse) · Power BI (data modelling, DAX measures, row-level security) · Python (pandas) for data checks and one-off analyses · A/B test readouts: power, confidence intervals, limits · Metric definitions and data-quality monitoring · Writing analyses for non-technical readers

Why it is written this way

Every bullet answers a question somebody asked

Analyst resumes often read as a list of outputs: reports built, dashboards maintained. This one is written the other way round. Each bullet names the question a stakeholder asked, the data used and the decision that followed, because an analytics hiring manager is checking whether a data analyst can define a problem before querying, not whether she can produce a chart.

Data-quality work gets the same weight as analysis

The duplicated checkout event and the shared revenue definitions take two of the five bullets in the current role. Finding and fixing data problems is a large part of an analyst's week and is the evidence hiring managers trust most, because it shows judgement about when a number should not be believed.

Tools appear in the skills line and inside the work

SQL, Power BI and Python are named in the skills section with the features actually used, and each also appears inside a bullet doing something. A bare tool list cannot be checked; a practical SQL test can. Only tools the applicant could use in a live exercise are listed.

Experiment readouts are described with their limits

Stating confidence intervals and the smallest detectable effect, and naming the test that should not have been called a win, shows statistical judgement at mid level without claiming a data scientist's toolkit. For a data analyst moving towards product work it is the most persuasive line on the page, so it sits in the current role rather than in a projects section.

Bullet rewrites, and the evidence behind them

Each rewrite was produced by asking an evidence question and using only the (fictional) answer. No number appears in a rewrite that is not in its evidence.

Before
Created reports and dashboards for the business.
Evidence question
How many dashboards, who reads them, how often, and what do they decide?
Evidence (fictional)
12 Power BI dashboards refreshed daily; the commercial director reads the weekly trading pack every Monday to set that week's promotions.
After
Maintain 12 Power BI dashboards refreshed daily, including the weekly trading pack the commercial director uses every Monday to set that week's promotions.
Before
Improved data quality across reporting.
Evidence question
Which problem did you find, how, and what was corrected as a result?
Evidence (fictional)
A 9 per cent drop in reported conversion traced to a duplicated checkout event on the Android app; three months of board figures corrected before the quarterly review.
After
Investigated a 9 per cent drop in reported conversion, traced it to a duplicated checkout event on the Android app and corrected three months of board figures before the quarterly review.
Before
Answered ad hoc data requests from stakeholders.
Evidence question
How many requests, from whom, and how did you make sure the analysis answered the real question?
Evidence (fictional)
Around 25 requests a month from trading, finance and marketing; each written up as question, data, answer and limits before anything is built.
After
Turn around 25 stakeholder questions a month into scoped analyses, writing up the question, the data, the answer and its limits before any chart is built.
Before
Experience with A/B testing.
Evidence question
How many tests, what did you produce, and did your reading change a decision?
Evidence (fictional)
Eight readouts on checkout and delivery pricing, each stating confidence intervals and the smallest detectable effect; one underpowered test was about to be read as a win.
After
Produced readouts for eight A/B tests on checkout and delivery pricing, stating confidence intervals and the smallest detectable effect, which stopped one underpowered test being read as a win.

Try it on your own bullets with the bullet workbench, which asks the same questions and never adds a figure you did not supply.

Transferable skills in this example

Turning a vague request into a defined question
Evidence: Scopes around 25 requests a month into question, data, answer and limits before building.
Where it helps: Business analysis, product management, consulting and any role that briefs technical teams.
Reconciling numbers that disagree
Evidence: Wrote shared definitions for revenue, returns and active customer that finance and marketing both use.
Where it helps: Finance operations, revenue assurance, audit support and data governance.
Explaining uncertainty to decision makers
Evidence: Experiment readouts with confidence intervals and the limits of each test, written for a trading team.
Where it helps: Product analytics, research operations, policy analysis and reporting roles outside e-commerce.
Automating a manual process
Evidence: Replaced a four-hour weekly Excel build with a scheduled query and a dashboard.
Where it helps: Operations, finance systems and reporting roles in any sector.

Occupation notes

Hard requirements

Right to work in the role's country is checked through the verified employer process at the stage required in that country and does not belong on the resume; if the advert asks about sponsorship, answer on the form or in the cover letter.
Most data analyst adverts name SQL and one BI tool as must-haves; state the ones you have used in real work, because a practical test usually follows. A degree is a hard requirement only where the advert says so; this fictional advert asked for SQL, a BI tool and A/B testing experience, not a qualification.

Vocabulary that belongs on the page

Window functions, CTEs, query plans (the SQL a practical test probes)
Metric definitions, single source of truth, semantic layer
Data-quality checks, reconciliation, duplicate events, tracking bugs
Dashboard refresh, scheduled queries, self-serve reporting
A/B test, confidence interval, statistical power, minimum detectable effect
Cohort, segmentation, funnel, conversion rate, churn
Data warehouse, star schema, fact and dimension tables

Pitfalls specific to this occupation

A tool list with no question answered: 'SQL, Python, Tableau, Excel' says what you have opened, not what you worked out with it.
Charts described without the decision they informed: 'built a sales dashboard' is a deliverable; who read it and what changed is the achievement.
Claiming statistical methods you could not defend; data analyst interviews ask you to explain one result in plain English.
Hiding data-quality work because it feels like admitting mistakes; fixing a double-counted event is exactly the judgement hiring managers look for.

UK and US differences

UK: UK adverts for this level say 'data analyst' or 'insight analyst' and expect SQL plus one BI tool; a practical SQL or Excel task is common, so list only what you can do live. Two pages is normal for three years' experience.

US: US resumes for the same work run to one page at this level and leave off date of birth, photo and full address. Many postings list a bachelor's degree; state yours plainly when you have one and lead with evidence when you do not.

Common questions

Should a data analyst resume include a projects or portfolio section?

Yes when work examples are confidential or thin. Describe each project as question, data, method and result, link a public notebook only if it is tidy, and never paste employer data into a public portfolio.

How do I show SQL skill without listing every function I know?

Put the features you actually use (joins, window functions, CTEs) in the skills line and show one query in context: which tables, what size, what question it answered. A practical test confirms the rest.

Published 6 November 2026 · Every person, employer and figure in this example is fictional. Drafted with AI assistance and checked against Luceria’s editorial standards.