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Zhiwen Zhao
Junior Data Engineer specializing in cloud ETL and big data platforms
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Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.
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Key Strengths
- Owned end-to-end data pipelines from Oracle ingestion through PySpark transformations to Azure Data Lake serving layer
- Hands-on data quality validation with route coverage and record-count checks (e.g., routes 1–6, 300–500 records per route)
- Debugged Spark execution issues (apply for Java error) by isolating root cause and using local checkpointing to break long lineage and reduce recomputation
- Designed/ran backfills via Airflow DAGs and manual Spark submit with parameterized date conditions; enabled parallel backfill execution
- Pragmatic technology selection in ambiguous projects (chose GraphFrames over NetworkX for Spark DataFrame compatibility)
- Implemented daily freshness checks using last-modified timestamps to ensure timely updates
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