Senior Data Engineer
Current• Designed, developed, optimized, and maintained ETL pipelines using python script, resulting in a 20% improvement in data processing efficiency.• Built pipelines for optimal extraction, transformation, and loading of data from various sources using SQL and PrestoDB 'big data' technologies, resulting in a 30% increase in data integration efficiency• Responsible for building ETL pipelines using Azure Data Factory (ADF) and Azure Synapse Analytics.• Developed and maintained data warehouses in big data solutions, ensuring data integrity and reliability.• Building of Scalable ETL pipelines using Azure Data bricks and Amazon glue Cloud technologies• Utilized cloud computing services and infrastructure to develop scalable data solutions, optimizing data delivery and infrastructure.• Configured a scheduling mechanism (Apache Airflow) to automate the execution of ETL processes at predefined intervals, leading to process reliability.• Reduce pipeline processing time by 30% by optimizing a complex SQL query• Architected more than ten end-to-end data ingestion and processing pipelines with Kafka, spark, and Presto • Designed and implemented Customer Value Management Datastore with Hive and Presto, which enabled more robust reporting capabilities for marketing campaigns. • Automated data ingestion workflows with Python scripting on Airflow, saving hours of manual work per week.• Developed data quality frameworks using Python to monitor data feeds for data completeness, trend checks, automated deduplication, and data reconciliation between sources, which enhanced data quality by 30% for critical business decisions. • Ensured data platform availability by promptly resolving workload and platform failures within SLA. Hive • SQL • Airflow • Python • Bash Script • Apache NIFI • Jira