2022 - Present

Data Engineering Sr Analyst at Accenture

● Designed and implemented a data pipeline to process data by integrating 20 million raw records from 12+ data sources using Python, SparkSQL and an internal ETL tool. ● Maintained data pipeline up-time of 99.8% while ingesting transactional data across 8 different primary data sources using SparkSQL, Redshift, S3, and Python. ● Data transfer from various sources to a particular target by transforming the data according to the business needs with the help of AWS RedShift, S3 bucket, EC2, Athena, Lambda, REST Services and Internal tools. ● Conducted regular data validation and cleansing processes, optimising database quality standards and reducing data

2021 - 2022

Data Engineering Analyst at Accenture

● Ingested data from 7 different sources using a combination of SparkSQL and Python to ingest into S3 and integrated with the different target systems (Salesforce, Cloudera, etc). ● Managed lifecycle elements of ETL development, from robust testing to final deployment. Employed data cleansing methods to filter the data according to business needs ● Promoted within 12 months due to strong performance and organizational impact. ● Developed and maintained data models to clean and filter the raw data, using tools like SQL, Python, and Pandas