Resume example
Data analyst resume example
A strong data analyst resume proves you can turn messy data into a decision someone acted on. This example shows an analyst with three years of experience in e-commerce and operations, with guidance on the tools, metrics and project framing that hiring managers look for.
Sample data analyst resume
Daniel Okafor
Data Analyst, Product and Marketing Analytics
Summary
Data analyst with 3 years of experience in SQL, Python and Tableau, supporting product and marketing teams at a mid-size retailer. Built the company's first self-serve KPI dashboard, now used by 120 staff weekly, and identified a churn driver that saved an estimated $380K a year.
Experience
- Built a Tableau KPI dashboard on top of 14 dbt models that replaced 9 weekly spreadsheet reports and is used by 120 staff each week.
- Ran a cohort analysis on 2.1M orders that linked late deliveries to a 22% higher churn rate, leading to a carrier change that saved an estimated $380K a year.
- Designed and analysed 11 A/B tests for checkout and email campaigns; the winning variants lifted revenue per visitor by 6.4%.
- Automated a monthly finance reconciliation in Python, cutting it from 2 days to 40 minutes.
- Wrote SQL queries across a 300-table warehouse to answer 25+ ad hoc requests a month from operations leads.
- Cleaned and standardised 4 years of shipment data, reducing reporting discrepancies between regions by 90%.
- Created a demand forecast in Excel that cut stockouts at 3 distribution centres by 18% over one quarter.
Education
B.Sc. in Statistics, Lakeview University, 2021
Skills
- SQL (PostgreSQL, BigQuery)
- Python (pandas, NumPy)
- Tableau
- Power BI
- dbt
- Excel (Power Query, pivot tables)
- A/B testing
- Cohort and funnel analysis
- Statistics and regression
- Data visualisation
- Stakeholder communication
Recommended template: Modern Professional. Open it in Resume Studio and replace the sample content with yours.
What hiring managers want to see
Data analyst roles vary from reporting-heavy to near data science, so read the posting carefully. Almost all of them test three things: SQL fluency, the ability to communicate findings to non-technical people, and evidence that your work changed a decision.
Your resume should show the full loop for at least two projects: the business question, the data and method you used, and what happened afterwards. "Built dashboards" is a task; "built a dashboard that replaced 9 manual reports" is an outcome.
- Tools with context: name the database, BI tool and language, and show them in bullets
- Scale: rows, tables, users of your dashboards, or number of stakeholders served
- Business outcomes: revenue, cost, time saved, churn, conversion or forecast accuracy
- Communication: presentations to leadership, documentation, or training you delivered
Writing your summary
Lead with your years of experience and core stack, then the domain you have worked in, then your single best result. Domain matters more than many candidates realise: an analyst who knows subscription metrics or supply chain data ramps up faster, so name it if it matches the role.
Data analyst keywords for ATS
Match the tool names in the posting exactly. If the listing says Power BI and you have used Tableau, list Tableau honestly and mention transferable BI experience in the summary rather than claiming a tool you have not used.
- SQL, Python or R, Excel, and the specific warehouse (BigQuery, Snowflake, Redshift)
- BI tools: Tableau, Power BI, Looker, Looker Studio
- Methods: A/B testing, regression, forecasting, segmentation, cohort analysis
- Data work: ETL, data cleaning, data modelling, dbt, data quality
Mistakes to avoid
- Listing tools without showing what you did with them
- Describing analyses without a result, recommendation or decision
- Including certificate courses above real project work
- Using charts or graphics on the resume itself; they rarely parse and take space from evidence
If you are changing careers into data
Use a projects section with two or three end-to-end analyses on public datasets, each with a link to a notebook or dashboard. Pick datasets close to the industry you are applying to, and write the project bullet like a work bullet: question, method, finding. Analytical work from a previous job, such as building reports in finance or operations, counts too and should be described in analyst terms.
Frequently asked questions
Do I need Python for a data analyst job?
Not always. SQL and a BI tool are required for almost every role; Python or R is common in larger or more technical teams. If the posting lists it as preferred rather than required, strong SQL and communication can still get you the interview.
Should I link to a portfolio?
Yes, especially early in your career. A link to two or three well-documented projects on GitHub or a portfolio site lets a reviewer see your SQL, your charts and how you explain findings.
How do I show impact if I do not know the business result?
Use the closest measurable effect: time saved, number of people using your report, reduction in errors, or the decision your analysis informed. Ask former managers if you are unsure; they often remember.
Are certifications worth listing?
List relevant ones, such as a cloud data or BI vendor certification, in a short section near the bottom. They support but do not replace project evidence.