A data analyst resume should connect a business question to the data, analysis, checks, and output used to answer it. This example shows SQL reporting and Power BI dashboards alongside validation and metric definitions, so the tools have evidence behind them.
Elena Brooks and the resume below are fictional. The sample follows a reporting-focused analyst in Raleigh, North Carolina, working with retail orders, shipments, and returns. Replace the sample employers, dates, tools, and deliverables with work you can explain and verify.
Elena Brooks
Data Analyst
(919) 555-0156elena.brooks@example.comRaleigh, NC
Summary
Data analyst working with retail order, shipment, and return data. Builds SQL reporting datasets and Power BI dashboards, checks source totals and join logic, and documents metric definitions for operations teams. Earlier experience includes Excel-based sales and stock reporting.
Experience
- Built PostgreSQL queries joining orders, shipments, and returns for weekly fulfillment reporting, checking join cardinality and reconciling totals against source extracts. (note 1)
- Developed a Power BI dashboard showing late shipments by warehouse and carrier, with date filters and drill-through views used in weekly operations reviews. (note 2)
- Documented the late-shipment definition with operations leads, including cutoff times and canceled-order exclusions, and applied it consistently across reports. (note 3)
- Investigated missing carrier codes in a reporting feed, isolated affected records, and added a validation query and exception report for the data team.
- Compared return reasons by product category and season, presenting patterns and sample-size limits to merchandising rather than attributing causes the data could not establish. (note 4)
- Prepared weekly sales and stock reports in Excel using pivot tables and lookup formulas, checking duplicate product codes and missing store submissions.
- Wrote report refresh instructions covering file locations, required columns, and exception checks so another assistant could run the weekly update.
Education
Skills
SQL (PostgreSQL), Power BI, Microsoft Excel, Data validation, Data reconciliation, Data visualization, KPI reporting, Data documentation
- Note 1:
The query work names the source tables and the checks that make the output credible.
- Note 2:
The dashboard has a specific subject, useful views, and an audience.
- Note 3:
Agreeing on a metric definition is analytical work, not just a documentation task.
- Note 4:
The wording distinguishes an observed pattern from a cause the analysis cannot establish.
Give the tools a business context
Elena's summary identifies order, shipment, and return data. Her experience then shows what she did with it: weekly fulfillment reporting, late-shipment views, data checks, and return analysis. That makes “SQL” and “Power BI” more informative than a list of tools alone.
Data analyst roles vary. A reporting-heavy opening may emphasize SQL and dashboards, while another may focus on experiments, forecasting, finance, or product behavior. Elena's sample supports reporting and operations analysis. It does not show machine learning, Python, or experiment design. Those skills should not appear simply because they are common in other analyst postings.
Use your own title and describe the analytical work underneath it. Elena's earlier title remains “Operations Reporting Assistant.” Her Excel reports are relevant, but the resume does not rewrite that job as a more senior analyst position.
Show how you checked the data
A query can run and still produce the wrong result. Elena's SQL bullet includes join cardinality and reconciliation against source extracts. Her missing-carrier-code bullet identifies a data issue and the checks she added. These details give an interviewer something concrete to discuss about her method.
Experience
- Built PostgreSQL queries joining orders, shipments, and returns for weekly fulfillment reporting, checking join cardinality and reconciling totals against source extracts. (note 1)
- Documented the late-shipment definition with operations leads, including cutoff times and canceled-order exclusions, and applied it consistently across reports.
- Investigated missing carrier codes in a reporting feed, isolated affected records, and added a validation query and exception report for the data team. (note 2)
- Note 1:
Checking the joins helps explain how Elena avoided counting records more than once.
- Note 2:
A validation query and exception report are specific deliverables. No unmeasured improvement percentage is needed.
Write the checks you actually performed: duplicate keys, missing records, totals, date boundaries, or another relevant condition. “Cleaned data” is less useful than naming the issue and your response. Do not add checks you know in theory but did not use in the work being described.
For a result you measured, preserve the original context. If a report refresh became faster, distinguish the query runtime from the total preparation process. If you cannot verify the before-and-after timing, keep the query, validation, or workflow change and leave the time claim out. The bullet-point guide explains how to choose credible evidence.
Explain what a dashboard helped someone review
“Built dashboards” leaves out the subject and audience. Elena's dashboard tracks late shipments by warehouse and carrier, has date filters and drill-through views, and is used in weekly operations reviews. Those facts explain the output's purpose without claiming that it caused a revenue increase or solved every fulfillment problem.
Experience
- Developed a Power BI dashboard showing late shipments by warehouse and carrier, with date filters and drill-through views used in weekly operations reviews.
- Compared return reasons by product category and season, presenting patterns and sample-size limits to merchandising rather than attributing causes the data could not establish.
If your analysis informed a decision, say what you delivered and who used it. Claim the decision or business outcome only when your contribution and evidence support that wording. An association in observational data does not automatically establish a cause. Elena's return analysis therefore mentions patterns and sample-size limits rather than stating why customers returned products.
Metric definitions also belong in the story. A late shipment depends on the cutoff and which orders count. Recording those choices can be as relevant as building the chart. Keep a definition brief on the resume, then be ready to explain the detail during an interview.
Keep the summary and skills consistent with the work
Elena lists SQL, Power BI, Excel, data validation, reconciliation, and documentation under the names an analyst posting would use. Every listed skill appears in her summary or an experience bullet. The list stays short enough to show the main match without asking the reader to infer expertise in an entire analytics stack.
Summary
Data analyst working with retail order, shipment, and return data. Builds SQL reporting datasets and Power BI dashboards, checks source totals and join logic, and documents metric definitions for operations teams. Earlier experience includes Excel-based sales and stock reporting.
Skills
SQL (PostgreSQL), Power BI, Microsoft Excel, Data validation, Data reconciliation, Data visualization, KPI reporting, Data documentation
A course or certification can show completed learning. It does not replace evidence of using a tool on a problem, and you do not need to add a credential section when you have no relevant credential. Elena's degree supports her background, while recent work receives most of the space.
Use projects honestly if you are entering the field
If your strongest analytics work is a personal or course project, label it that way. Describe the question, dataset, method, validation, output, and limitations. Do not describe a practice dashboard as client work or claim a commercial result from a public dataset.
A portfolio can help when it contains work you can share and explain. Use public or permitted data, and check that the link opens without requiring an unexpected login. Leave confidential employer data and internal screenshots out. Elena's example has no portfolio link because her recent work supplies the evidence. The personal projects guide shows how to present a project when it is the stronger example.
Tailor to the kind of analysis the posting needs
For a SQL-heavy opening, Elena could lead with joined datasets and validation. For a dashboard-focused role, the Power BI output and metric definitions could come first. Neither version should add Python or experimentation as an implied match.
Read the posting's business context as closely as its tool names. Retail operations work may transfer to another setting, but the resume should explain the shared analytical task rather than pretend the industries are identical. The tailoring guide explains how to choose that evidence. For a role centered on building software, compare the different emphasis in the software engineer example.