Can you describe a project where you had to ingest advertising data from multiple platforms (e.g., TM360, DB360, TradeDesk) and handle data quality issues in custom offline data?
💡 Model Answer
Situation: Amazon requested a unified view of their advertising spend across multiple platforms—TM360, DB360, TradeDesk, and custom offline data. Task: Build an ingestion pipeline that could handle heterogeneous schemas, large volumes, and data quality issues. Action: I designed an ETL workflow using AWS Glue crawlers to discover schemas, Spark jobs to transform and clean data, and Airflow to orchestrate the pipeline. For the custom offline data, we implemented a data quality framework that flagged missing values, outliers, and duplicate records, and routed them to a manual review queue. The cleaned data was loaded into Amazon Redshift for analytics. I also set up automated alerts for data quality failures. Result: The pipeline processed 10 TB of data daily with a 99.9% success rate, reduced data errors by 30%, and enabled real‑time dashboards that were delivered on schedule.
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