Data & Analytics · Mid level · Updated August 2026

    Business Intelligence Analyst Resume Example

    BI has a haunted house in every company: hundreds of reports, no owners, three versions of revenue. A BI analyst gets hired to end that, so the resume should read like a record of order imposed on sprawl. This example is built that way; open it in the builder and refit it to your stack.

    Adoption numbers are the whole pitch

    A dashboard is not an outcome. Someone opening it every morning to make a decision is. That means the metrics that sell BI work are usage metrics: viewers, retirements, and requests deflected, not the count of dashboards built.

    Do

    • Report monthly active viewers, not dashboards shipped
    • Count what you retired: 90 reports down to 12
    • Show deflection: ad-hoc requests halved after training
    • Tie one dashboard to one recurring decision it drives

    Don't

    • Claim 'created 50+ reports' as if volume were the win
    • Say 'improved visibility' with no usage evidence
    • List chart types and slicers as accomplishments
    • Ignore the spreadsheet chaos your work replaced

    Notice the inversion: "created 50+ reports" and the example's lead bullet ("consolidated 90+ reports into 12") describe opposite instincts, and hiring managers are scarred by the first one. Report factories created the sprawl; you want to be the person who ended it.

    One number, one definition: the semantic-model story

    Ask a BI manager what actually hurts and you'll hear versions of the same story: two executives walk into a meeting with two different values for the same KPI. The work that fixes this (star schemas, a documented measure library, certified datasets) is the most valuable thing you can put on a BI resume, precisely because it's invisible in a portfolio screenshot.

    Give it the vocabulary it deserves. "Built the claims semantic model so 'claims paid' means one thing across five departments" tells a reviewer you understand that BI's product is trust, not charts. The same one-number discipline built in dbt instead of DAX is the analytics engineer example's whole page, if your stack leans that way. Governance work belongs here too: row-level security, workspace standards, and a certification process are what let a company scale self-serve without scaling chaos.

    Modeling beats charting on the skills line

    Order your skills the way the job actually values them: modeling and query skills first (DAX, SQL, star schemas), platform mechanics second (Power Query, deployment pipelines), visualization last. It reverses how most BI resumes are written, which is exactly why it works.

    Round it out with evidence you can operate on both sides of the desk: a SQL bullet that shows you can source your own data, and a training bullet that shows you can teach the users. The BI analyst who reduces tickets by teaching is more valuable than the one who closes them faster. And if your strongest stories are analyses that changed decisions rather than reporting people trust, compare the data analyst example before choosing which title to apply under.

    Frequently asked questions

    Do I need both Power BI and Tableau?

    You need depth in one and literacy in the other. Concepts transfer (modeling, calculated measures, publishing workflows), and reviewers know it. Go deep on whichever your target market uses; in insurance, healthcare, and most Microsoft shops that's Power BI.

    What numbers prove a dashboard mattered?

    Usage and displacement. Monthly active viewers, the number of reports or spreadsheets your work retired, ad-hoc requests deflected, and hours saved in recurring processes like month-end close. A dashboard nobody opens is a liability, so adoption is the metric that separates you.

    Is DAX worth calling out separately from Power BI?

    Yes. Anyone can arrange visuals; a documented measure library and a well-shaped star schema are what make the numbers trustworthy and fast. Listing DAX (and Power Query) separately signals you build the engine, not just the paint.

    Which matters more on the resume: visual design or data modeling?

    Modeling, and it isn't close. Layout taste is easy to teach and easy to copy; a semantic model that ends metric disputes across departments is the hard, durable work. Show design sense in your portfolio, but spend your bullets on the model.

    Ready to make it yours?

    Open this example in the builder, swap in your own work, and download a polished, ATS-ready PDF.

    Read the full example as text

    Owen Fitzgerald — business intelligence analyst resume example

    Minneapolis, MN

    Summary

    BI analyst with five years turning report sprawl into governed, certified dashboards people actually open. Strong in Power BI, DAX, and the unglamorous work of making one number mean one thing.

    Work Experience

    Business Intelligence Analyst · North Elm Insurance

    2022 – Present · Minneapolis, MN

    • Consolidated 90+ orphaned reports into 12 certified Power BI dashboards; monthly active viewers doubled while report count fell 85%.
    • Built the claims semantic model (star schema plus a documented DAX measure library) so 'claims paid' means one thing across five departments.
    • Implemented row-level security and workspace governance so regional managers see exactly their own book of business.

    Reporting Analyst · GrainBelt Foods

    2020 – 2022 · Minneapolis, MN

    • Replaced emailed spreadsheets with a scheduled Power BI app that 200+ field reps open every morning.
    • Wrote the SQL views behind finance's month-end close pack, cutting close reporting from three days to one.
    • Trained 30 business users on self-serve basics; ad-hoc report requests fell by half within a quarter.

    Reporting Intern · GrainBelt Foods

    2019 – 2020 · Minneapolis, MN

    • Automated the plant's daily production tally from line-system CSV exports into a self-refreshing Excel model, replacing hand-keyed numbers.
    • Wrote the first documentation for month-end reporting; the next hire ramped up in a week instead of a month.

    Projects

    Minneapolis 311 explorer · Power BI, public data

    • Public Power BI report mapping Minneapolis 311 service requests by neighborhood, type, and resolution time.
    • Embedded on a neighborhood association's site and cited in a community budget discussion.

    Education

    B.S. Management Information Systems · University of Minnesota

    2016 – 2020 · Minneapolis, MN

    Certifications

    Microsoft Fabric Analytics Engineer (DP-600) · Microsoft

    2025

    Tableau Certified Data Analyst · Tableau

    2022

    Languages

    • English · Native
    • German · Professional

    Skills

    • BI platforms: Power BI, DAX, Power Query, Tableau
    • Data: SQL, SQL Server, Snowflake, Excel
    • Practices: Star schemas, Report governance, Row-level security, User training