• Worked on projects at Meta, VMWare, and the government• Created ETL pipelines using Python and SQL for the videos infrastructure team• Working closely with data scientists and software developers to understand data needs and design models and dashboards to support them• Conducted performance tuning and optimization on pipelines to reduce processing time• Worked on large-scale data warehouse migration from an on-premises infrastructure to an AWS cloud-based solution• Tools used: Pyspark, Python, SQL, Terraform, AWS Glue, Glue Catalog, S3, Presto, Delta Lake, Iceberg, Pandas, Hive, HDFS, Informatica, Git, Github

• Created ETL queries for Hive and HQL scripts to ensure CCPA data compliance• Used React.js, HTML, CSS to create a friendlier UI for scoping decision changes made by the team• Tools used: PySpark, Hive, SQL, Lucy, React, CSS

• Worked on project to identify fraudulent purchases using PySpark, PySpark’s MLlibpackage and Python’s Pandas library• Tasked with identifying optimal K-Means cluster sizes for Machine Learning projects • Onboarding training on Big Data technologies including PySpark, Hive, MapReduce, Scala, and HBase• Tools used: PySpark, Pandas, Python, Elasticsearch, Matplotlib

• Developed property valuations for commercial, industrial, and multi-family improvements using sales comparison, income, and cost approach methods• Reviewed and analyzed market data, trends, property tax records, and other relevant data