omar-gamal99/talabat_bootcamp
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
AirflowBigQueryCloud Storage ConfigurationData EngineeringETLGoogle Cloud Storage
AhmedMarzouk01
PROFILE
Ahmedmarzouk01
Ahmed Marzouk developed an end-to-end Airflow-based ETL pipeline for the omar-gamal99/talabat_bootcamp repository, enabling automated data movement from PostgreSQL to Google Cloud Storage and BigQuery. He designed and implemented a new Airflow DAG in Python to orchestrate extraction, transfer, and loading processes, addressing both data flow and storage configuration. Ahmed improved reliability by fixing syntax in extraction scripts, correcting BigQuery table naming, and standardizing GCS bucket and file names. His work reduced manual intervention and improved data consistency, demonstrating practical data engineering skills in Airflow, Python, and cloud storage while delivering a maintainable, production-ready analytics pipeline.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
1
Lines of code
144
Activity Months1
Your Network
22 people
Work History
May 2025
7 Commits • 1 Features
May 1, 2025In May 2025, delivered an end-to-end Airflow-based ETL pipeline for omar-gamal99/talabat_bootcamp, enabling reliable data flow from PostgreSQL to Google Cloud Storage and BigQuery. Implemented a new Airflow DAG to run a Python function and orchestrate the complete ETL process, including data transfer to GCS and loading into BigQuery. Accompanied by targeted fixes to the data transfer DAG and storage naming/configuration to improve reliability and maintainability (syntax fixes in extraction, corrected BigQuery table naming, clarified DAG naming, and standardized GCS bucket/file naming). These changes reduce manual intervention, improve data consistency, and accelerate analytics readiness.
7 Commits • 1 Features
May 1, 2025In May 2025, delivered an end-to-end Airflow-based ETL pipeline for omar-gamal99/talabat_bootcamp, enabling reliable data flow from PostgreSQL to Google Cloud Storage and BigQuery. Implemented a new Airflow DAG to run a Python function and orchestrate the complete ETL process, including data transfer to GCS and loading into BigQuery. Accompanied by targeted fixes to the data transfer DAG and storage naming/configuration to improve reliability and maintainability (syntax fixes in extraction, corrected BigQuery table naming, clarified DAG naming, and standardized GCS bucket/file naming). These changes reduce manual intervention, improve data consistency, and accelerate analytics readiness.
May 2025
Activity
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Quality Metrics
Correctness94.2%
Maintainability94.2%
Architecture91.4%
Performance91.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AirflowBigQueryCloud Storage ConfigurationData EngineeringETLGoogle Cloud StoragePostgreSQLPython
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