Resume Research · Data Analysts

15 Resume Mistakes I Keep Seeing in Data Analyst Candidates

SECTION 01The 5 Formatting Mistakes

Formatting is the first thing recruiters see. If it's hard to read, they will move on.

• 1. Wall of Text: No bullet points, no spacing. Recruiters scan in 15 seconds.
• 2. Inconsistent Fonts: Using 3 different font sizes and styles. Keep it clean.
• 3. Missing Contact Info: No LinkedIn, no GitHub, no email. How will they reach you?
• 4. Too Long: A 3-page resume for 2 years of experience. Keep it to one page.
• 5. Unprofessional Email: Using "cool_boy_99@email.com" instead of "firstname.lastname".

SECTION 02The 5 Content Mistakes

Content is where most candidates lose the interview. Focus on impact, not tasks.

• 6. Vague Descriptions: "Worked on data analysis." What did you analyze? What was the outcome?
• 7. No Metrics: "Improved dashboard." By how much? How many users?
• 8. Listing Skills Instead of Using Them: "SQL, Python, Tableau." Show how you used them in a project.
• 9. Ignoring Soft Skills: Data Analysts need communication. Add a line about stakeholder management.
• 10. No Portfolio: You have no link to your GitHub or Tableau Public. This is a missed opportunity.

SECTION 03The 5 Skills Mistakes

Skills are important, but only if you can prove them.

• 11. "Excel" Only: Excel is a baseline skill, not a differentiator. Show SQL and Python.
• 12. No SQL Proof: SQL is the #1 skill for Data Analysts. Show a project where you used it.
• 13. No Python/Bi Tools: Show familiarity with Python (Pandas) or a BI tool (Tableau, Power BI).
• 14. No Testing/Documentation: Show that you can document your analysis or test your SQL queries.
• 15. No Deployment Context: Where did your dashboard live? How was it used?

SECTION 04A before/after from the batch

Data Analyst — Vertex Systems (2020-2024)
- Worked with SQL and Excel.
- Built dashboards using Tableau.
- Analyzed customer data.
- Used Python for some tasks.
- Worked in an agile team.

Data Analyst — Vertex Systems (2020-2024)
- Wrote complex SQL queries to analyze 2M+ rows
  of customer data; reduced query time from 2 mins to 30 secs.
- Built a Tableau dashboard used by 50+ sales reps;
  increased daily active usage by 25%.
- Used Python (Pandas) to automate weekly reporting;
  saved 5 hours of manual work per week.
- Documented all data pipelines and SQL queries;
  reduced onboarding time for new analysts by 40%.
- Collaborated with 3 stakeholders to define KPIs;
  improved dashboard adoption by 30%.

SECTION 05The 20-minute checklist

Content checklist:
☐ One SQL query example (impact on data size).
☐ One dashboard example (users, adoption).
☐ One automation example (hours saved).
☐ Numbers on 3+ bullets.
☐ LinkedIn and GitHub links in header.
☐ No vague "worked on" bullets.

Positioning checklist:
☐ Opening line = role + stack + one impact number.
☐ Every keyword defensible live.
☐ One page for 2-5 years of experience.
☐ Three proofreading passes done.
☐ Filename: Firstname-Lastname-DA-Resume.pdf

SECTION 06Interview Q&A

SECTION 07Test yourself — what stuck?

SECTION 08Frequently asked questions

SECTION 09Related reads