Data analyst résumé: show the decision, not just the dashboard

What recruiters look for

• Core tools named the way job posts do: SQL, Excel (pivot tables, Power Query), Python (pandas), Power BI or Tableau.
• Business context: which team used your work and what they did with it.
• Data scale: rows, sources, refresh frequency.
• For freshers: two or three end-to-end projects on real datasets, with a link to the notebook or dashboard.

Bullet rewrites

• Before

  Created dashboards in Power BI.

  After

  Built a Power BI sales dashboard used by 25 regional managers weekly, replacing a 6-hour manual Excel report.

• Before

  Analysed customer data.

  After

  Found that 18% of churned customers had raised 3+ support tickets in their last month; the retention team now calls them proactively.

• Before

  Did a project on IPL data.

  After

  Analysed 1,000+ IPL matches in Python to test whether toss decisions affect results; published the notebook and a Tableau story with 2K views.

Structure

• Summary naming your tools and the kind of data you work with (sales, product, finance, operations).
• Experience or projects, each with the question, the method in one phrase, and the outcome.
• A skills section split into Querying, Analysis, Visualisation and Statistics.
• Certifications only if they match the job (e.g. Google Data Analytics, Microsoft PL-300).

Common mistakes

• Listing tools without showing a single result from using them.
• Kaggle tutorial projects with no question of your own.
• Calling yourself a data scientist for analyst roles, or the reverse.

Templates for data & analytics

• Midnight Tech
• Emerald Ledger
• Charcoal Columns

Questions