omar-gamal99/talabat_bootcamp
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
AirflowBigQueryCloud ComputingCloud Data EngineeringDAG DevelopmentDAG Management
Aya Hany
PROFILE
Aya Hany
Omar Gamal developed automated data pipelines and orchestration solutions for the omar-gamal99/talabat_bootcamp repository, focusing on end-to-end data integration and reliability. He built an Airflow-based workflow that extracts data from PostgreSQL, stages it in Google Cloud Storage, and loads it into BigQuery, coordinating multiple DAGs with master orchestration and cross-DAG triggers. Using Python and leveraging Airflow’s DAG management capabilities, Omar also implemented API-driven data ingestion for payments and addressed reliability by fixing the GCSToBigQueryOperator’s file path handling. His work delivered analytics-ready datasets and robust automation, demonstrating practical depth in cloud data engineering, ETL design, and workflow automation.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
2
Lines of code
280
Activity Months1
Your Network
22 people
Work History
May 2025
19 Commits • 2 Features
May 1, 2025May 2025 highlights for omar-gamal99/talabat_bootcamp: Delivered Airflow-based automation and end-to-end data integration improvements that enhance data reliability, automation, and analytics readiness. Key features delivered include: 1) Airflow Greetings DAG for scheduled task orchestration and demonstration of recurring task execution. 2) End-to-end PostgreSQL -> GCS -> BigQuery data pipeline with Airflow-based orchestration, including master DAGs and cross-DAG triggers to coordinate multiple related workflows. 3) Data ingestion enhancement paths such as API-driven payments data loads into BigQuery as part of the pipeline. 4) Reliability improvement via a GCSToBigQueryOperator fix to correctly reference the source file path, ensuring successful loads from GCS to BigQuery. Major technical achievements are complemented by concrete commits and deliverables that demonstrate practical business value and scalable data engineering practices.
19 Commits • 2 Features
May 1, 2025May 2025 highlights for omar-gamal99/talabat_bootcamp: Delivered Airflow-based automation and end-to-end data integration improvements that enhance data reliability, automation, and analytics readiness. Key features delivered include: 1) Airflow Greetings DAG for scheduled task orchestration and demonstration of recurring task execution. 2) End-to-end PostgreSQL -> GCS -> BigQuery data pipeline with Airflow-based orchestration, including master DAGs and cross-DAG triggers to coordinate multiple related workflows. 3) Data ingestion enhancement paths such as API-driven payments data loads into BigQuery as part of the pipeline. 4) Reliability improvement via a GCSToBigQueryOperator fix to correctly reference the source file path, ensuring successful loads from GCS to BigQuery. Major technical achievements are complemented by concrete commits and deliverables that demonstrate practical business value and scalable data engineering practices.
May 2025
Activity
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Quality Metrics
Correctness85.2%
Maintainability86.2%
Architecture85.2%
Performance82.0%
AI Usage24.2%
Skills & Technologies
Programming Languages
Python
Technical Skills
AirflowBigQueryCloud ComputingCloud Data EngineeringDAG DevelopmentDAG ManagementDAG creationDAGsData EngineeringETLGoogle Cloud StoragePostgreSQL
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