Data Analyst Resume Examples

Data Analyst Resume Example

Emphasize business questions answered, decision support, and repeatable analytics assets (dashboards, models, definitions). Show SQL fluency and stakeholder impact.

Structure

1. Summary: domain, toolset (SQL, Python/R), data sizes
2. Technical Skills: SQL (window functions), dbt, ETL/ELT, BI tools
3. Experience: analyses that changed decisions or moved KPIs
4. Projects: reproducible notebooks/dashboards
5. Education/Certs: relevant degrees, Google DA, etc.

Impact Bullets

• Built churn prediction with logistic regression (AUC 0.82), enabling −11% churn via targeted campaigns.
• Standardized revenue attribution definitions across 5 teams; discrepancies −90%.
• Migrated 20 legacy SQL reports to dbt models; refresh time −67%.

ATS Keywords

SQL, dbt, Snowflake/BigQuery/Redshift, ETL/ELT, Airflow, Python, Pandas, R, Tableau, Power BI, Looker, Cohort Analysis, A/B Testing, Experimentation, KPI, Forecasting, Dashboards, Data Modeling, Dimensional Modeling.

Sample Summary

Data Analyst skilled in SQL/dbt and BI (Looker, Tableau), turning complex datasets into decisions. Delivered churn model (AUC 0.82) and standardized revenue metrics across 5 teams.

One-Page Template (HTML)

<h1>Priya Singh</h1>
<p>Remote • priya@analytics.dev • linkedin.com/in/priyasingh • github.com/priyads</p>

<h2>Summary</h2>
<p>Data Analyst (SQL/dbt, Tableau) focusing on revenue and retention analytics.</p>

<h2>Skills</h2>
<p>SQL (CTEs, windows), dbt, BigQuery, Python (Pandas), Tableau, A/B Testing</p>

<h2>Experience</h2>
<h3>Shoply — Data Analyst (2023–Present)</h3>
<ul>
  <li>Churn model (AUC 0.82) drove −11% churn via lifecycle campaigns.</li>
  <li>Revenue definitions standardized; discrepancies −90%.</li>
</ul>
                    

Common Mistakes

• “Built dashboards” without stating who used them or what changed.
• Only tool lists; no business outcomes.
• No reproducibility (notebooks/SQL omitted).