Pradeep Kumar Shivapur

AXA.be (Accenture)

AXA.be is one of the major insurers in Belgium & EU

Technologies Stack: Azure Databricks, Data Factory, pyspark, Log Analytics, Application Insights, ComosDB NOSQL, EventHub.

Description:

As a lead data engineer, I engaged in Analysis, Design, architecting for the solution which is used in migrating various applications from Legacy On prem to Azure environment

Expertise in Apache Structured streaming using Databricks & EventHub
Expertise in designing & architecting CosmosDB applications.

FPL, USA (Accenture)

Florida Power & Light Company (FPL), the principal subsidiary of NextEra Energy Inc (formerly FPL Group, Inc.), is the largest power utility in Florida

Technologies Stack: AWS EMR, Glue, SNS, CloudWatch Logs, Lambda, Apache Spark, Hive

Description:

As a lead, I engaged in Analysis, Design, Architecting for the solution which is used in migrating various applications from Legacy On prem to AWS environment

Refactored all the applications to adhere to use AWS services such as AWS CloudwatchLog, Glue, EMR & SNS
Migrating Kafka based applications into AWS MSK
Migrated On prem specific Java applications to Lambda functions using S3 event trigger

Wholesale Lending Data Services (Wells Fargo India and Philippines)

Wells Fargo & Company is an American multinational financial services company with corporate headquarters in San Francisco, California

Technology Stack: CDH (Cloudera Distribution of Hadoop), Apache Spark, Hive, Hbase, Scala, Shell Scripting, Autosys, Oracle Exadata

Description:

WLDS is responsible for building Data Lake for commercial capital and Data integration to move towards advancement in Data Analytics, Business intelligence and Data science
This brings the benefit of improved speed to Value, speed to Market due to availability of all datasets in central repository
I engaged in data modelling, building data mart and making the process more optimized and less time consuming
Implemented reports for reconciliation when data moves in heterogeneous environment using Scala
Identified the process improvement opportunity and made the EMG refresh time reduced by 30%, there by achieving quick turnaround time for the business to operate on
All BCP exercises are handled and performed smoke tests in order to be ready at any given time
Proactively took up automating a report which is essential part of any system when data flows from one platform to another using Scala
Successfully migrated EMG DataMart jobs from EDL1 to EDL2 which includes new Autosys JIL creation, rollback in case to hold on the release, new scripts which would eliminate the use of Spark shell programs as a part of secure code practice, tuning the long running jobs to make data quickly available to End users, converted multiple Hive queries to Spark SQL in order to get the benefit of Spark framework with the use of Spark Dataframe APIs using Scala

Microsoft Azure Big Data Technical advisory (Mindtree)

Technology Stack: HDInisght, Azure Data Factory, Azure Function, Azure Data Lake Storage, CosmosDB

Description:

As a Technical specialist for Azure Big Data, I have handled Big Data problems ranging from Deployment, Development, Configuration management, Performance tuning, Migration and Decommissioning

Advised Azure vendors on how they can deploy their applications on cloud using HDInsight, Azure Blob, Azure Data Lake store, installing custom applications in the cluster using custom action scripts
Advising partners to choose between Standalone and Enterprise grade clusters
Hive troubleshooting and optimization
Helping the vendors upload huge number of files onto ADLS using ADLS tool
Optimize clusters using Ambari UI
Advising the vendors during Development for various decision-making process
Helping the partners to achieve migration from old versions to reap the benefits of newer ones
Automation of daily cluster management in Azure cloud service
Designing the system so that it can be reused without no hassles
Designed and developed Spark systems with basic and Advanced data sources
Increased revenue by upselling and recommending products.

eSurplus Automation (Mindtree)

AIG eSurplus is an underwriting application for property and casualty users in USA

Technology Stack: Apache Sqoop, Hive

Description:

Query execution on mainframe data amounts to lot of cost, hence we decided to import all that structured data using Sqoop
Then the data warehouse is created in Hadoop environment to perform ETL jobs, which is of high latency but is fault tolerant

AIG TPAIR, AIG receives claims and drafts data from various TPAs (Mindtree)

This information uploaded by the TPAs and team handles this huge data either to accept or reject claims involving steps such as FTP tracking, Coverage validation, Input Preprocessing, Load &MU

Technology Stack: Spring MVC, RAD, WAS70

Developed the modules which help the TPA to manage the claims submitted
Fixing application and code scan issues using security filter of all severities

eSurplus T&M

Is a Camilion based underwriting web based application that is used for processing Excess and Surplus business using several strategic commercial insurance services like REM, Booking, and Publishing and many more

Technology Stack: Core Java, Oracle 10g, DB2

Worked on enhancements, QA support, maintenance Activities and bug fixes

• Graduated with Distinction