Data analyst resume example and writing guide

Hiring managers for analyst roles want evidence of two things: that you can get correct numbers out of messy data, and that someone made a better decision because of your work. Tools matter, but they are the means, not the result.

Your resume should show the questions you answered, the data and tools you used, and who used the output. A line such as “built a Power BI dashboard” is a start; “built the Power BI dashboard the regional managers use in every Monday meeting” is much stronger, because it shows the work was trusted and used.

Below is a complete example for an analyst with about three years of experience. The names, companies and figures are invented for illustration — replace them with your own, and only include numbers you can explain.

Header: Tan Wei Ling · Data Analyst · Kuala Lumpur · 016-234 5678 · weiling.tan@example.com · linkedin.com/in/tanweiling · github.com/tanweiling.

Summary: Data analyst with three years of experience in retail and e-commerce, turning sales and customer data into reports and dashboards that managers use to plan stock and promotions. Writes SQL daily, builds dashboards in Power BI and uses Python (pandas) for cleaning and one-off analysis. Known for checking numbers against source systems before they reach a meeting.

Data Analyst, Kedai Harian Retail Sdn Bhd (fictional), Kuala Lumpur · June 2024 – present.

• Built and maintain the weekly sales dashboard in Power BI for 45 outlets, used by area managers to review performance every Monday.
• Wrote SQL queries to combine point-of-sale, inventory and promotion data, replacing a manual Excel process that took the finance team two days each month.
• Analysed two years of promotion results and showed that bundle deals sold more units than straight discounts for most product categories, which changed how the next quarter’s promotions were planned.
• Set up automated data checks that flag missing outlet uploads before the dashboard refreshes, so errors are caught before managers see them.

Junior Business Analyst, Lembah Online Sdn Bhd (fictional), Petaling Jaya · August 2022 – May 2024.

• Produced daily and monthly order reports in Excel and Google Sheets for the marketing and operations teams.
• Cleaned and merged customer records from three marketplace exports, removing about 8,000 duplicate entries.
• Answered ad-hoc data requests from five teams, usually within the same day, and kept a shared log of queries so common requests could be reused.

Education: Bachelor of Science in Statistics, a Malaysian public university, 2022. Final-year project: forecasting monthly electricity demand with time-series models.

Skills: SQL (PostgreSQL, MySQL) · Power BI (DAX, Power Query) · Microsoft Excel (pivot tables, XLOOKUP, Power Query) · Python (pandas, Matplotlib) · Tableau (basic) · Data cleaning · Reporting · Stakeholder communication.

Projects: Kuala Lumpur rental listings analysis — scraped and cleaned about 5,000 public rental listings, compared rent per square foot by area, and published the findings with charts on GitHub.

• Junior or fresh graduate: “Statistics graduate with internship experience preparing weekly reports in Excel and SQL for a logistics company. Built three portfolio projects in Python and Power BI, including a public dashboard on Malaysian retail data.”
• Experienced: “Data analyst with five years in banking, building credit and collections dashboards in Tableau and SQL for teams of up to 40 people. Comfortable presenting findings to senior managers and turning vague questions into clear analysis.”
• Career changer: “Accounts executive moving into data analysis, with four years of month-end reporting and reconciliation in Excel. Completed SQL and Power BI courses and rebuilt our team’s expense report as an automated dashboard.”

Pick the bullets that match the job and rewrite them with your own work and figures.

• Reporting: “Automated the monthly management report with Power Query, cutting preparation time from two days to about two hours.”
• Reporting: “Standardised definitions of ‘active customer’ and ‘repeat order’ across three teams so reports finally matched.”
• Dashboards: “Designed a Tableau dashboard for the collections team showing overdue accounts by age and region, refreshed daily.”
• Dashboards: “Replaced eight separate Excel reports with one Power BI dashboard with filters for each department.”
• SQL: “Wrote and documented about 40 SQL queries used by the team for recurring requests, so new analysts could run them without help.”
• SQL: “Rewrote a slow reporting query with proper joins and indexes, reducing run time from several minutes to under ten seconds.”
• Python: “Used pandas to clean and merge a year of survey responses, handling inconsistent answers and missing values.”
• Analysis: “Found that most late deliveries came from two warehouses, which led operations to change their dispatch cut-off times.”
• Stakeholder work: “Met each department head to understand what decisions they needed data for before building their reports.”
• Stakeholder work: “Presented quarterly sales findings to the management team in plain language, with one recommended action per slide.”
• Data quality: “Traced a mismatch between the dashboard and the finance system to a timezone error in the export and fixed it at the source.”

If you are a fresh graduate or changing careers, projects are your evidence. Two or three solid projects are better than ten tutorial copies, and a project with your own question and data stands out more than the same public dataset everyone uses in online courses.

Describe each project in the same order a manager would ask about it:

• The question: what were you trying to find out, and why would anyone care?
• The data: where it came from, how big it was, and what you had to clean.
• The method and tools: SQL, Excel, Python, Power BI or Tableau — and why you chose them.
• The finding: one or two clear conclusions, stated in plain language.
• The link: a GitHub repository with a README, or a published dashboard, so the reader can check your work.

Recruiters search for tool names, so write them exactly as they appear in the job post. List only tools you could use in a test, because many analyst interviews include a SQL or Excel exercise.

• Data tools: SQL, Microsoft Excel, Power BI, Tableau, Looker Studio, Google Sheets.
• Programming: Python (pandas, NumPy, Matplotlib), R if you have used it.
• Databases: PostgreSQL, MySQL, Microsoft SQL Server, BigQuery — name the ones you have actually queried.
• Analysis skills: data cleaning, reporting, dashboard design, A/B testing, forecasting, descriptive statistics.
• Working skills: requirement gathering, presenting findings, documentation.

• Listing tools with no example of what you built with them.
• Describing the chart instead of the decision: say what changed because of your analysis.
• Skill ratings such as “Python 4/5” — they cannot be checked and say very little.
• Including portfolio projects with broken links or no README.
• Sharing real company data or screenshots of internal dashboards in your portfolio. Recreate the idea with public or sample data instead.

Analyst roles vary a lot. A bank or insurer may care most about SQL, accuracy and working with controlled data; an e-commerce company may care about speed, marketing data and experiments; a shared services centre may focus on Excel, Power BI and recurring reports. Read the job post and move your most relevant bullets and tools to the top.

Match the industry where you can. If you have analysed sales data and the job is in retail, lead with that. If the post mentions Tableau and you know both Tableau and Power BI, list Tableau first. Keep the facts the same — only the order and emphasis change.