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
Developed automated data integration solutions for the omar-gamal99/talabat_bootcamp repository, focusing on Airflow-based orchestration to streamline analytics workflows. Built a scheduled Greetings DAG to demonstrate recurring task execution and delivered an end-to-end data pipeline moving data from PostgreSQL through Google Cloud Storage into BigQuery. Leveraged Python and Airflow to coordinate multiple DAGs, including a master DAG and cross-DAG triggers, enabling reliable, automated data movement. Enhanced the pipeline with API-driven payments data ingestion and improved reliability by fixing the GCSToBigQueryOperator to ensure correct file path referencing. Demonstrated practical skills in ETL, cloud data engineering, and scalable workflow management.
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 peopleWork 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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