Data Engineer resume example

• Built Azure data pipelines that load operational extracts into curated tables, using SQL merge logic with explicit keys and late-arrival rules; tested reprocessing without duplicating records.
• Added SQL reconciliation and quarantine checks to the data pipelines, retaining rejected rows with a reason code; traced an upstream schema change before it reached the reporting layer.
• Added source-to-target row reconciliation and late-arrival handling to a data pipeline; replayed an interrupted partition without duplicating completed records.
• Owned an Airflow ETL recovery path using partitioned SQL fixtures, resolved duplicate loads and completed replay without changing downstream business keys.
• Built Python data-pipeline checks using Spark row reconciliation, reduced late-partition investigation from 45 to 18 minutes and published the Azure rerun guide.

• Published certified datasets and ownership for 15 recurring business questions. 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.

• Implemented incremental models and partition pruning for the 12 largest transforms
• 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 · Data pipelines · Source-to-target reconciliation · Partition replay

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