Passionate about transforming raw data into valuable insights, I am a Data Engineer with 3 years of experience specializing in Azure-based big data solutions. I have a strong background in ETL/ELT pipelines and cloud-bas…
Passionate about transforming raw data into valuable insights, I am a Data Engineer with 3 years of experience specializing in Azure-based big data solutions. I have a strong background in ETL/ELT pipelines and cloud-based analytics.
I have successfully designed and optimized data pipelines for large-scale datasets, leveraging Azure Data Factory, Databricks, Synapse Analytics, Hive, Sqoop and Apache Spark. My expertise in SQL, Python, and PySpark enables me to build scalable, high-performance data solutions that drive business growth.
<p>Developed and maintained scalable ETL pipelines, improving data processing efficiency by 30%.<br>
Led collaboration efforts with data analysts to design optimized data models, enhancing reporting capabilities and reducing query times by 25%.<br>
Implemented data validation and cleansing procedures, increasing data accuracy by 20%.<br>
Managed Azure Synapse Analytics for data warehousing, reducing storage costs by 15%.</p>
• Developed and maintained scalable ETL pipelines, improving data processing efficiency by 30%.
• Led collaboration efforts with data analysts to design optimized data models, enhancing reporting capabilities and reducing query times by 25%.
• Implemented data validation and cleansing procedures, increasing data accuracy by 20%.
• Managed Azure Synapse Analytics for data warehousing, reducing storage costs by 15%.
• Developed scalable big data processing pipelines using Hadoop, Spark, and other distributed computing technologies to process 10+ Petabytes/month.
• Designed and implemented scalable data solutions, ensuring efficient data ingestion, transformation, and storage.
• Imported large data from RDBMS to HDFS and handled incremental load using Sqoop. Analyzed data sets by writing Hive queries.
<p>Developed scalable big data processing pipelines using Hadoop, Spark, and other distributed computing technologies to process 10+ Petabytes/month.<br>
Designed and implemented scalable data solutions, ensuring efficient data ingestion, transformation, and storage.<br>
Imported large data from RDBMS to HDFS and handled incremental load using Sqoop.<br>
Analyzed data sets by writing Hive queries.</p>
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