Senior BI Analyst resume example

• As workstream lead, built SQL data modeling and ETL checks for a compliance reporting mart, separating event time from load time; reconciled control totals before publishing the dataset to dashboards.
• With responsibility for the review standard, created QuickSight dashboards with row-level access and data visualization that exposed missing evidence; used Python checks to compare exported totals with the reporting mart after each refresh.
• As workstream lead, published a Tableau revenue dashboard using the same SQL definitions as the finance reconciliation. Validated refunds, time zones, and late-arriving orders before release, giving finance and account teams a shared view without conflicting totals.
• Built a Power BI measure dictionary and reconciled DAX totals at detail and aggregate levels; tested row-level access with accounts representing different reporting teams.
• Owned a SQL data-model reconciliation using known reference totals, resolved inconsistent Tableau and QuickSight measures and completed a shared metric dictionary.
• Built Python ETL checks using data-quality assertions, reduced weekly dashboard reconciliation from 95 to 40 minutes and published the statistics validation notebook.

• Trained 52 business users on governed self-service analysis and reviewed adoption; aligned review standards and decision owners across the participating teams. Working with partner teams, kept the lower-effort process in place after handoff.
• 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.

• Implemented freshness, reconciliation, and ownership checks for 31 executive metrics; aligned review standards and decision owners across the participating teams
• 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

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