LUCERIA
Sign in
Data and analytics · Career change · UK conventions

Data analyst (career change from finance): resume example, explained

This fictional example shows an accounts assistant with five years of month-end reporting applying for a junior data analyst role: the finance work is described as the analysis it already is, SQL and Power BI are shown producing results in the day job, and public-data projects point at the target employer. 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 accounts assistant with five years in a fictional building-supplies distributor's finance team, applying for a junior data analyst role at a fictional regional water company after 14 months of evening study in SQL and Power BI. Every employer, figure and date is fictional.

Editable version of this example (.docx)

The example document

Daniel Osei-Mensah

Accounts Assistant moving into data analysis · SQL and Power BI in daily use · AAT Level 3

Nottingham, UK · 07700 900 782 · daniel.osei-mensah@example.com

Profile

Accounts assistant with five years of month-end reconciliation and management reporting in a fictional building-supplies distributor, moving into data analysis after 14 months of evening study in SQL, Power BI and statistics. Already automates the finance team's reporting: a SQL-based margin report replaced 11 linked spreadsheets, and a matching query finds supplier invoice mismatches once found by hand. Seeking a junior data analyst role where accuracy and clear reporting matter.

Experience

Accounts Assistant (Management Reporting) — Fictional Builders Merchants Group

June 2021 – present · Finance team of six; 14 branches; around 3,500 supplier invoices a month.

  • Reconcile the sales ledger, stock movements and bank each month across 14 branches, resolving every variance above the finance manager's threshold before the close on working day five.
  • Rebuilt the monthly branch margin report from 11 linked spreadsheets into one SQL query and a Power BI dashboard, cutting the build from two days to about three hours.
  • Wrote a SQL query matching supplier invoices to purchase orders on quantity and price; it flagged 38 mismatched invoices in its first quarter that manual checks had passed.
  • Present the monthly margin pack to the finance manager and three regional managers, explaining movements by branch and product group in plain language.
  • Documented every report's source, refresh timing and owner in a shared register, so the team no longer depends on one person's spreadsheet.

Accounts Administrator — Fictional Builders Merchants Group

February 2020 – June 2021

  • Processed around 600 purchase invoices a month with an error rate below one per cent at audit sampling.
  • Maintained the fixed asset register and prepared year-end audit schedules on time in both years.

Education

  • AAT Level 3 Diploma in Accounting, 2021
  • Evening study since September 2025: SQL for analysis, Power BI modelling and introductory statistics (fictional online provider); projects below

Projects

  • Household water use by region (own time, 2026): combined two open datasets of about 180,000 rows in SQL, cleaned inconsistent region codes and built a Power BI report on consumption trends; the write-up states the limits of the sampling.
  • Branch margin model (work, 2025): the rebuild above, with a definition of gross margin agreed in writing with the finance manager.
  • Supplier invoice matching (work, 2026): the query above, tested against 200 historic invoices to confirm it caught every known mismatch.

Skills

SQL (joins, grouping, CTEs; written against the accounting system's reporting database) · Power BI (data model, measures, scheduled refresh) · Excel (pivot tables, Power Query, lookups; five years of daily use) · Reconciliation and variance analysis · Statistics basics: distributions, sampling, confidence intervals · Documentation and agreed data definitions

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.

  1. Before

    Responsible for monthly reconciliations.

    Evidence question

    Which ledgers, across how many sites, to what standard and by when?

    Evidence (fictional)

    Sales ledger, stock movements and bank; 14 branches; every variance above the manager's threshold resolved before the close on working day five.

    After

    Reconcile the sales ledger, stock movements and bank each month across 14 branches, resolving every variance above the finance manager's threshold before the close on working day five.

  2. Before

    Produced the monthly margin report.

    Evidence question

    What did it replace, how did you build it, and how long does it take now?

    Evidence (fictional)

    Replaced 11 linked spreadsheets with one SQL query over the accounting system's reporting database and a Power BI dashboard; build time fell from two days to about three hours.

    After

    Rebuilt the monthly branch margin report from 11 linked spreadsheets into one SQL query and a Power BI dashboard, cutting the build from two days to about three hours.

  3. Before

    Checked supplier invoices for errors.

    Evidence question

    How did you check them, what did the check find, and had anything been missed before?

    Evidence (fictional)

    A SQL query matching invoices to purchase orders on quantity and price; 38 mismatched invoices found in its first quarter that manual checks had passed.

    After

    Wrote a SQL query matching supplier invoices to purchase orders on quantity and price; it flagged 38 mismatched invoices in its first quarter that manual checks had passed.

  4. Before

    Learning SQL and Power BI in my spare time.

    Evidence question

    What have you built with them, on what data, and what did you find?

    Evidence (fictional)

    Two open datasets of about 180,000 rows combined in SQL; inconsistent region codes cleaned; a Power BI report on household water consumption; sampling limits written up.

    After

    Built a Power BI report on household water consumption from two open datasets of about 180,000 rows, cleaning inconsistent region codes in SQL and writing up the limits of the sampling.

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

  • Reconciliation and variance investigation

    Evidence: Monthly three-way reconciliation across 14 branches; a query that found 38 mismatched invoices manual checks had passed.

    Where it helps: Data-quality work in any analyst role: checking that two systems agree is the same discipline as reconciling ledgers.

  • Accuracy under a fixed deadline

    Evidence: Month-end close by working day five for five years; audit sampling error rate below one per cent.

    Where it helps: Reporting roles with regulatory or board deadlines, finance analytics and operations analysis.

  • Explaining numbers to non-finance managers

    Evidence: Presents the monthly margin pack to regional managers and explains movements by branch and product group.

    Where it helps: Any analyst role that reports to operational or commercial teams, and business-partnering roles.

  • Replacing manual processes with queries

    Evidence: An 11-spreadsheet report became one documented query and dashboard; a manual invoice check became an automated match.

    Where it helps: Reporting automation, finance systems and junior analytics engineering roles.

Occupation notes

Hard requirements

  • A verified UK employer may check right to work during recruitment; identity documents are not resume content. Answer any sponsorship question on the application form.
  • The fictional advert asked for SQL and one BI tool and no specific degree, as many UK data analyst adverts do; show those tools in use rather than listing them. Where an advert does require a degree or a Level 4 qualification, say plainly what you hold.
  • Routes into the role in England include degrees, college courses and apprenticeships: the data analyst apprenticeship standard (Level 4, typical duration 24 months) is one recognised route, and the National Careers Service lists it beside university and college options in its data analyst-statistician profile. None is required where the advert does not ask.

Vocabulary that belongs on the page

  • Reconciliation, variance, three-way match (finance terms that translate directly into data-quality checks)
  • Joins, grouping, CTEs, reporting database
  • Data model, measures, scheduled refresh (Power BI)
  • Data cleansing, inconsistent codes, deduplication
  • Agreed metric definition, such as a single definition of gross margin
  • Open data, public datasets, sampling limits
  • Dashboards and reports, which the Level 4 data analyst standard describes as 'infographics, reports, tables, dashboards and graphs'

Pitfalls specific to this occupation

  • Writing 'currently learning SQL' with nothing built: a data analyst reader wants one query that did real work, however small.
  • Describing accounts work only in accounting terms (journals, accruals, prepayments) so the analytics reader cannot see the reconciliation and reporting skills inside it.
  • A projects section built on tutorial datasets everyone has seen; choose open data relevant to the employer and state what the data could not show.
  • Dropping the finance years to look like a 'pure' analyst: five years of accurate, deadline-driven reporting is the strongest evidence a career changer has.

UK and US differences

  • UK: Public-sector and utility employers in the UK often use a scored application form or supporting statement; the bullets here are written so each can be pasted as evidence against a criterion.
  • UK: The Level 4 data analyst apprenticeship standard cited below is published by Skills England for England; check the equivalent route if you are applying in Scotland, Wales or Northern Ireland.

Common questions

Do I need a data analyst qualification to move from accounts into analytics?

Not where the advert does not require one. Degrees, college courses and the Level 4 data analyst apprenticeship are recognised routes, but a hiring manager mainly wants evidence you can query data, check it and explain it, which this resume shows from the day job and from projects.

Should I use a skills-based resume for a career change into data?

Only if your relevant experience is scattered. With five continuous years of reporting work, a chronological resume with a clear headline and a projects section reads as more credible and parses cleanly in applicant tracking systems.

Sources

Keep going

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.