Data Analyst Resume: Product Reporting and SQL Example
A fictional data analyst resume showing metric definitions, cohort analysis and reporting improvements, with notes on attribution and comparable measurements.
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Ananya Rao
Data Analyst
Bengaluru, Indiaananya.rao@example.com+91 98XXX XXXXX
Summary
Data analyst working with product and operations teams on merchant activity, cohort reporting and data quality. Uses SQL and Python to define measures, investigate changes and produce reviewable analysis.
Experience
Data Analyst · Example Northstar Merchant Services
2023-03 – PresentBengaluru, India- Maintain merchant-activity reporting for 12,000 accounts, defining qualifying activity and retention windows with product colleagues.
- Built a dbt model with duplicate and missing-key checks so weekly cohort reports use consistent account populations.
- Investigated settlement-related support events alongside retention patterns and documented hypotheses for product testing.
- Reduced one daily reporting query from 52 to 18 minutes on the same date range and warehouse setup, reconciling totals before rollout.
Business Analyst · Example Orchard Retail
2021-07 – 2023-02Bengaluru, India- Analysed return reasons by product category and checked inconsistent reason codes with operations.
- Prepared experiment summaries with group sizes, return-rate definitions and uncertainty notes for the product analyst.
- Automated recurring category tables in Looker and documented filters used in weekly reviews.
Education
- 2017 – 2021
B.Tech, Computer Science
Example Lakeside University
Skills
- Languages:
- SQL, Python (pandas, NumPy), R
- Tools:
- dbt, Looker, Tableau, Airflow, BigQuery
- Methods:
- A/B testing, Cohort analysis, Regression, Funnel analysis
Languages
English, Hindi, Kannada
Explain the metric definition
The retention model distinguishes accounts registered from accounts with qualifying activity. That prevents a familiar reporting mistake: treating different populations as the same denominator. Describe the definition and the users of the report.
Separate association from causation
The failed-settlement investigation groups support events and retention outcomes. It supports an investigation, not proof that one email caused churn or that a fix generated a precise annual revenue gain. State what the analysis established and what required a further test.
Make performance comparisons fair
The query comparison uses the same date range and environment. A faster query on a smaller dataset is not the same result. Keep enough context in your notes to explain the change and its correctness checks.
All 22 templates on this resume
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