LUCERIA
Sign in
Data and analytics · Mid level · US conventions

Business intelligence analyst: resume example, explained

This fictional example shows a business intelligence analyst with five years in retail reporting applying for a BI analyst role in healthcare operations: each dashboard is tied to the meeting it feeds, modeling and definitions work sits beside the dashboards, and retiring unused reports is treated as an achievement. 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 business intelligence analyst with five years in a fictional regional grocery chain's finance and operations reporting team, applying for a BI analyst role at a fictional outpatient healthcare network. Every employer, figure and date is fictional.

Editable version of this example (.docx)

The example document

Marcus Delgado-Price

Business Intelligence Analyst · Power BI and SQL Server reporting · 5 years in retail operations and finance

Columbus, OH · (614) 555-0147 · marcus.delgado-price@example.com

Profile

Business intelligence analyst with five years building the reporting layer for a fictional 42-store regional grocery chain: a SQL Server data mart, 18 Power BI dashboards used by store, merchandising and finance leaders, and the weekly shrink and labor reports that replaced emailed spreadsheets. Asks what decision a report supports before building it, and retires reports nobody opens. Seeking a BI analyst role in healthcare operations where reporting standards and data definitions need the same discipline.

Experience

Business Intelligence Analyst — Fictional Midwest Grocers Inc.

April 2023 – present · Fictional 42-store regional grocery chain; BI team of three reporting to the finance director.

  • Design and maintain 18 Power BI dashboards on a SQL Server data mart, including the weekly shrink report store directors review every Tuesday and the monthly labor-cost pack for the finance director.
  • Interviewed 11 department heads to define each report's purpose, retired nine reports with no viewers in 90 days and consolidated the rest, taking the catalog from 31 dashboards to 18 with no requests to bring any back.
  • Built and documented the data mart's star schema (sales, inventory, labor and promotion facts; store, item and calendar dimensions), with a definitions page for each measure that finance and merchandising signed off.
  • Automated the Monday shrink and markdown reports, replacing six spreadsheets store managers emailed each week with a scheduled refresh that delivers by 6 a.m. instead of mid-afternoon.
  • Traced a 3 percentage-point gap between the shrink dashboard and the finance ledger to markdowns counted twice in the data mart, corrected the model and added a reconciliation check to every refresh.

Reporting Analyst, Store Operations — Fictional Midwest Grocers Inc.

June 2021 – April 2023

  • Produced weekly sales and labor reports for 42 stores in Excel and SQL, and converted the labor report into a shared Power BI dashboard in 2022.
  • Wrote the first data dictionary for the point-of-sale extract, resolving three conflicting definitions of 'transaction' used across teams.

Education

  • BS Business Administration (information systems concentration), Fictional State University, 2021
  • Vendor certification in Power BI data analysis, 2023

Selected decisions informed

  • Shrink dashboard (2024): store directors used the by-department view to move two high-shrink categories behind the service counter in 14 stores; shrink in those categories fell the following quarter.
  • Labor-cost pack (2025): finance used the hours-versus-sales trend to change the scheduling guideline for Sunday shifts across the chain.

Skills

SQL (T-SQL: window functions, CTEs, stored procedures for mart loads) · Power BI (data modeling, DAX measures, row-level security, deployment pipelines) · Dimensional modeling (star schema, conformed dimensions) · Report requirements gathering and stakeholder interviews · Data reconciliation and definitions governance · Excel (Power Query, pivot tables) for ad hoc analysis

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

    Created dashboards for business users.

    Evidence question

    How many dashboards, on what data, who uses them, and when?

    Evidence (fictional)

    18 Power BI dashboards on a SQL Server data mart; store directors review the weekly shrink report every Tuesday; the finance director reads the monthly labor-cost pack.

    After

    Design and maintain 18 Power BI dashboards on a SQL Server data mart, including the weekly shrink report store directors review every Tuesday and the monthly labor-cost pack for the finance director.

  2. Before

    Managed the reporting catalog.

    Evidence question

    What did you do to it, based on what evidence, and what did users say afterwards?

    Evidence (fictional)

    Interviewed 11 department heads; retired nine reports with no viewers in 90 days; consolidated the rest; the catalog fell from 31 dashboards to 18; no requests to restore any.

    After

    Interviewed 11 department heads to define each report's purpose, retired nine reports with no viewers in 90 days and consolidated the rest, taking the catalog from 31 dashboards to 18 with no requests to bring any back.

  3. Before

    Automated reports.

    Evidence question

    Which reports, what did they replace, and what changed for the people who prepared or read them?

    Evidence (fictional)

    The Monday shrink and markdown reports; replaced six emailed spreadsheets prepared by store managers; the scheduled refresh delivers by 6 a.m. rather than mid-afternoon.

    After

    Automated the Monday shrink and markdown reports, replacing six spreadsheets store managers emailed each week with a scheduled refresh that delivers by 6 a.m. instead of mid-afternoon.

  4. Before

    Resolved data discrepancies.

    Evidence question

    What was the discrepancy, how did you find the cause, and what stops it recurring?

    Evidence (fictional)

    A 3 percentage-point gap between the shrink dashboard and the finance ledger; markdowns counted twice in the mart; model corrected; a reconciliation check added to every refresh.

    After

    Traced a 3 percentage-point gap between the shrink dashboard and the finance ledger to markdowns counted twice in the data mart, corrected the model and added a reconciliation check to every refresh.

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

  • Requirements gathering with non-technical leaders

    Evidence: Interviewed 11 department heads to define what each report was for before rebuilding the catalog.

    Where it helps: Business analysis, product ownership and internal consulting in any sector, including the healthcare operations role targeted here.

  • Dimensional data modeling

    Evidence: Designed and documented a star schema with signed-off measure definitions.

    Where it helps: Analytics engineering, data warehouse and finance systems roles.

  • Reconciling reported figures to the ledger

    Evidence: Found and fixed double-counted markdowns and built the check into every refresh.

    Where it helps: Financial planning and analysis, audit support and revenue-cycle reporting in healthcare.

  • Simplifying what an organization measures

    Evidence: Cut 31 dashboards to 18 by retiring unused reports without losing any needed view.

    Where it helps: Operations leadership, performance-management and transformation roles.

Occupation notes

Hard requirements

  • Authorization to work in the US is confirmed during hiring and is not resume content; if an application asks whether you will need sponsorship, answer there.
  • BI postings typically name the BI platform and database as must-haves (here Power BI and SQL Server); list the ones you have used at work, because a practical dashboard or SQL exercise often follows. A degree is a hard requirement only where the posting says so. O*NET places business intelligence analysts in Job Zone Four, where 'most of these occupations require a four-year bachelor's degree, but some do not', so state your degree plainly if you have one and lead with evidence if you do not.

Vocabulary that belongs on the page

  • Data mart, star schema, fact and dimension tables, conformed dimensions
  • Measures and KPIs, DAX, calculated columns, semantic model
  • Scheduled refresh, gateway, row-level security, deployment pipeline
  • Report catalog, usage metrics, report retirement
  • Shrink, markdown, labor hours versus sales (retail BI measures; swap in your sector's)
  • Reconciliation to the general ledger, variance, definitions sign-off
  • Requirements interview, the decision a report supports, report owner
  • Self-service BI versus governed reporting

Pitfalls specific to this occupation

  • A tool list with no question answered: 'Power BI, Tableau, SQL, SSRS' says what you have used, not what anyone decided with it.
  • Charts described without the decision they informed: 'built an executive dashboard' is a deliverable; the Tuesday meeting and the categories moved behind the counter are the achievement.
  • Counting dashboards as if more were better; a business intelligence hiring manager knows an uncurated catalog is a maintenance problem.
  • Skipping modeling and definitions work because end users never see it; it is exactly what separates a business intelligence analyst from a report builder.

UK and US differences

  • UK: In the UK the same work is often advertised as 'BI analyst', 'insight analyst' or 'MI analyst'; a two-page resume is normal and 'labor' and 'modeling' take British spellings.
  • US: US postings use 'business intelligence analyst', 'BI analyst', 'BI developer' and 'reporting analyst' for overlapping work; mirror the posting's title in your headline when the duties match. Keep the resume to one page at this level, with no photo, date of birth or street address.

US occupation: Business Intelligence Analysts (O*NET code 15-2051.01, O*NET 31.0 Database (O*NET-SOC 2019 taxonomy)). Occupation code, title, Job Zone note and a short description paraphrase adapted from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration, under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Luceria made these adaptations; the Department has not approved or endorsed this page. Uses the occupation code and title, Job Zone Four education note and a short paraphrase of the occupation description; the fictional resume and its figures are Luceria's own work. Licence.

Common questions

Is a business intelligence analyst the same as a data analyst?

They overlap. BI analysts usually own the reporting layer many people rely on: the data model, the dashboards, refresh schedules and definitions, while data analysts more often answer one-off questions. O*NET lists business intelligence analysts separately (15-2051.01), describing work such as generating reports and maintaining dashboards and databases.

Should a BI resume say how many dashboards I built?

Only alongside who uses them and what for. A count on its own rewards volume; a count with named readers, a review cadence and one decision each informed shows the judgment employers are hiring for.

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.