Data Specialist resume example

• Built SQL transformations for member eligibility and claims records, defining effective-date joins and the grain of each analytical table; checked that retroactive eligibility changes did not duplicate claim totals.
• Reconciled analytical tables with source claims before publishing the Tableau report; traced a total mismatch to reversed claims and documented the approved treatment with the reporting owner.
• Defined the grain and refresh schedule of an analytics model, added SQL tests for duplicate business keys, and reconciled dashboard totals to the underlying transaction ledger.
• Defined dbt data quality checks for eligibility date ranges, missing claim keys and reversed payments; added monitoring for failed checks and verified that quarantined records did not enter the published semantic layer.
• Owned a claims-reporting reconciliation using SQL and dbt tests, resolved duplicate eligibility joins and completed the data-quality review before dashboard publication.
• Published Tableau and Power BI metric definitions using reconciled source records, reduced weekly reporting queries by 30% and added monitoring for late adjustments.

• Published analysis assumptions, limitations, and decision owners for 18 studies. Kept turnaround time within the agreed operating window.
• 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 tested semantic layer for 27 recurring business metrics
• 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 · Analytics modeling · Data quality tests · Dashboard reconciliation

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