Divya Sree Velugula

• Responsibilities:
• Actively involved in all phases of Data life cycle creating, optimizing, updating and maintaining data models for various applications and systems
• Lead design reviews of schemas and relevant metadata to ensure consistency, quality, accuracy and integrity
• Worked in gathering information and requirements from multiple teams to build and design data schemas and project plans
• Monitored log4j vulnerability throughout the bank by extracting, cleaning and loading data into well-defined
• HIVE tables for better analysis
• Worked on retrieving data from Kafka queues and Splunk
• Processed the streaming data using best optimization techniques in Spark to attain better performance
• Loaded the processed data to HDFS and built HIVE tables on top of it
• Analyzed the data in order to find out any anomalies as part of Data Loss Prevention practices and provide alerts when found
• Developed Python scripts to perform analysis on the Hive data to obtain best performance when executed in local
• Build Spark projects in IntelliJ and deploy then using Jenkins which is in fact connected through the BitBucket repositories
• Scheduled daily and hourly jobs on crontab and Oozie scheduler
• Involved in troubleshooting errors for legacy applications on the server
• Actively worked in migrating the existing functionalities and project from CDP6 to CDP7( server-server migration)
• Computing: Hadoop, HIVE, HDFS, Spark, Pyspark, Scala, Python, SQL, Oozie, Apache Kafka, Splunk, Unix Shell Scripting, Crontab
• Integrated third-party APIs into existing applications for additional functionality.
• Maintained source control repository in order to track changes between versions of codebase.
• Conducted full lifecycle software development from planning to deployment and maintenance.
• Jenkins, Intellij, BitBucket, Horizon Jira Board etc.
• Optimized stored procedures for increased efficiency in query results.
• Assisted in the creation of technical documentation such as design documents, testing plans.
• Communicated with clients to define program needs and requirements, explaining potential challenges and costs.

• Actively involved in all phases of data science life cycle including data extraction, data cleaning, data processing and data visualization
• Involved in Data processing, Data analysis building models to get insights from structured and unstructured data
• Worked on writing complex Sql queries to consult, update and reorganize the data
• Extensive hand-on on different libraries like NumPy, Pandas, Matplotlib, Plotly, ScikitLearn
• Hand-on experience with various Hive optimization Techniques to store and process the data efficiently.
• Developed scenarios and executed them to automate monthly PBM business cycle
• Handled Huge amount of data related to PBM Domain to generate monthly reports and provide analytical solutions for legacy systems
• Met with stakeholders, product teams and customers throughout system development lifecycle.
• Scheduled ongoing performance quality assurance checks for software applications and automated performance test scripts.
• Analyzed code and corrected errors to optimize output.
• Employed integrated development environments (IDEs).
• Wrote user manuals and other documentation for roll-out in customer training sessions.

Computing: Python, Data Structures, Python Libraries, Hadoop, Hive, Apache Spark, Mainframes, Tableau, SQL Server, Eclipse, DB Source.

Project1: Glaxo Smith Kline

• Involved in the analysis, design, and development phases of the Big Data Systems and continuously involved
• Using Cloudera as a distribution platform for Hadoop, created HIVE Tables on top of datasets in staging layers
• Extensively used Cloudera Stream-sets to transfer Raw data files into HDFS foundation layer
• Worked on developing Python Scripts to automate ETL process and for integrating third party tools
• Created Impala Tables as the target tables to load data into the integration layer
• Used Parquet file format in all target tables in Impala for better performance
• Involved in Building data frames and RDD’s using Spark SQL and Spark Core
• Used Various Hive performance optimization techniques
• Used Spark core and Spark SQL for data transformations.
• Analyzed solutions and coding fixes for software problems.
• Provided technical support to customers regarding product usage and troubleshooting issues.
• Coordinated with project managers to meet development timelines and plan testing.

Project2 : Shell Oil & Gas

• Responsibilities:
• Developing data pipelines using Azure data factory to ingest data from on-premise systems to Azure Data
• Lake storage core
• Implementing the data transformation or processing logics using Azure databricks service with spark and
• Scala
• Used azure databricks notebooks to write the spark code
• Loading the transformed datasets into Azure SQL-DB using Azure data factory
• Writing the stored procedures on SQL-DB stage layer to handle incremental updates

Computing: Python 3, Anaconda, Data Structures, Hadoop, Spark Scala, Hive, Impala, Nifi, Teradata, Sql Server,GIT, Microsoft Azure, Azure DataLake Storage, Azure Data Factory, Azure DataBricks, Spark Scala, Azure SQL-DB