نبذة عني
Data Engineer with 1 year of experience in designing, developing, and managing scalable and cost-efficient data pipelines on the AWS Cloud platform. Proficient in data ingestion, transformation, and orchestration using A…
Data Engineer with 1 year of experience in designing, developing, and managing scalable and cost-efficient data pipelines on the AWS Cloud platform. Proficient in data ingestion, transformation, and orchestration using Apache Airflow, Amazon Redshift, and Apache Spark. Skilled in performance optimization, workflow automation, and maintaining high data quality. Adept at collaborating with cross-functional teams to deliver automation-driven data solutions that support data-driven decision making.
الخبرة
AWS Data Engineer
Developed scalable ETL workflows using Apache Airflow and Redshift, improving pipeline reliability by 30%. Resolved data issues and job failures quickly using ServiceNow, ensuring smooth daily operations. Optimized Apache Spark jobs, improving processing performance and reducing execution time by 30% and compute costs by 15%. Improved Amazon Redshift performance with partitioning, clustering, and SQL tuning, reducing query time by 30%. Automated monitoring and data validation using Python, reducing manual efforts by 40%. Collaborated with analysts to align pipelines with business goals, reducing ad-hoc requests by 30%. Maintained pipeline health by tracking job dependencies, retries, and failure alerts to ensure uninterrupted data processing.
Trainee Engineer
Migrated data from on-premises Sybase database to AWS cloud using AWS DMS. Performed both full load migration and incremental data sync using Change Data Capture (CDC). Validated migrated data to ensure accuracy, completeness, and consistency post-migration. Ensured 100% SLA adherence across 700+ pipelines through proactive job monitoring. Monitored and managed Apache Airflow DAGs, identifying pipeline failures and performing root cause analysis.
المشاريع
Optimized Data Pipeline & Warehousing for E-commerce Analytics (AWS
Enhanced an e-commerce ETL pipeline using AWS Glue, Redshift, and Kinesis. Technologies: AWS Glue, Redshift, Kinesis, S3, Lambda Optimized an ETL pipeline for e-commerce data, boosting processing speed by 40% and accuracy by 25% Integrated data from multiple sources into S3 for scalable storage and analysis Implemented Amazon Redshift as a data warehouse, enabling fast querying and analytics of large e-commerce datasets Scalable ETL Pipeline for Customer Transactions • Optimized an ETL data pipeline for customer transactions, improving data ingestion, transformation, and storage Technologies : AWS Glue, Redshift, S3, Python • • • Designed and implemented a scalable ETL pipeline, enhancing data processing efficiency by 50%. Utilized ETL processes to integrate and clean data for visualization through Power BI dashboards. Improved data consistency and accuracy by 20% through validation checks and optimized queries