Data Analyst resume example

• Used SQL and Python for data analysis of service requests, checking duplicates, missing timestamps, and category changes; resolved data quality issues before comparing turnaround time across teams.
• Created a Tableau visualization with documented denominators and drill-down views; reconciled its totals against a Power BI reference report and explained differences caused by refresh timing.
• Documented the denominator and exclusion rules for conversion metrics, investigated differences between SQL extracts and the dashboard, and shared cohort-level findings with the business owner.
• Owned Tableau data-quality checks using reconciled source totals, resolved duplicate customer records and released a dashboard with explicit denominator definitions.
• Redesigned a weekly visualization using stakeholder task tests, reduced time to locate an outlier from 6 to 2 minutes and documented the excluded populations.

• Designed a matched cohort analysis for a customer education program. Kept adoption moving without creating new support or operating load.
• Investigated a discrepancy between a published metric and its source records, traced the transformation that changed the population, and corrected the calculation with a reproducible query.
• Compared the last successful data refresh with a failed run, separated missing source data from transformation errors, and reran only the affected interval. Kept the original query and corrected result together for review.

• Built a governed weekly operating dashboard for 14 leaders
• Generated synthetic source records with duplicates, late arrivals and corrected values; wrote assertions for row counts and key uniqueness, and recorded the expected effect of each case on the reported metric.
• Compared the analytical output with a manually calculated reference table, traced differences to a transformation step, and retained a data dictionary and rerun instructions alongside the corrected query.
• Owned the synthetic-data validation using a manually calculated reference, resolved duplicate-key inflation and completed a notebook that reproduces the corrected totals.

Relevant coursework: Algorithms, operating systems, databases, computer networks

SQL · Python · Data modeling · Data quality · Visualization · Stakeholder communication

• Published an independent methods note using reproducible queries, explaining the data grain, excluded records and sensitivity of the result to a changed denominator.