About

Vamshi Kaladandi is a Senior Data Engineer with over 6 years of experience in the field. He has a strong programming background and hands-on experience in all the big data technology. Vamshi has a good understanding of Hadoop Ecosystem, including Spark, MapReduce, Hive, Sqoop, HBase, Kafka, Scala, Java, Python, Data Visualization, Data Analytics, Clusters (Cloudera, Azure Databricks, HDInsight), and Cloud Platforms such as AWS and AZURE. Vamshi has experience in designing and maintaining Datahub/Data lake, assisting with the business case and eliciting requirements, and working with Data Management tools/practices and Data modeling. He has strong analytical, interpersonal, and presentation skills, and good programming knowledge in Shell scripting, Linux, and Python. Vamshi has the ability to work in a fast-paced, action-oriented environment, and has experience in sourcing, integration, ingestion, transformation, and analytics of discrete data sources. Currently, Vamshi is working as a Senior Data Engineer at Walmart. Previously, he worked as a Data Engineer 3 at Paypal, where he interacted with different business users to know about their use cases, ingestion tables, PII data, and built data models accordingly for faster insertion/updating of data. Vamshi also designed and developed modules to compare data across multiple properties and generate metric stats and daily alerts. He designed and developed code for metadata automation for generating audit table inserts for onboarding source. Before Paypal, Vamshi worked as a Senior Data Engineer at Fractal. He worked on projects for Procter & Gamble (P&G) and MARS – DCOM US MW (DCOM). For P&G, Vamshi used Spark with Python, Azure Databricks, Azure Blob Storage, Azure Data Lake Store (ADLS gen 2), Azure Data Factory (ADF), SQL Synapse, and Key vault. He re-architected the ABD system to modernization and modularize and developed a system that could scale with varying data volumes and came up with a configurable approach. For MARS – DCOM US MW (DCOM), Vamshi used Spark with Python, Azure Databricks, Azure Blob Storage, Azure Data Lake Store (ADLS gen 2), Azure Data Factory (ADF), SQL Synapse, and Key vault. He combined different data sources in a single data layer so other applications could