Summary
Overview
Work History
Education
Skills
Technical Skillset
Languages
Timeline
Generic

SUNIL MEHTA

Chicago,IL

Summary

Professional Summary:

With over 10 years of experience in Information Technology and 7+ years specializing in Big Data using the Hadoop ecosystem, I bring expertise in analysis, design, development, testing, deployment, and integration using SQL and Big Data technologies. I have hands-on experience with major Hadoop components like HDFS, YARN, MapReduce, Hive, Impala, Pig, Spark, Kafka, and more.

Results-focused data professional equipped for impactful contributions. Expertise in designing, building, and optimizing complex data pipelines and ETL processes. Strong in SQL, Python, and cloud platforms, ensuring seamless data integration and robust data solutions. Known for excelling in collaborative environments, adapting swiftly to evolving needs, and driving team success.

I have strong knowledge of distributed systems, MapReduce, and Spark processing frameworks, along with experience in ETL methods for data extraction, transformation, and loading. I have successfully deployed Big Data applications using Talend on AWS and Microsoft Azure and optimized cloud services with AWS, including EC2, Redshift, Glue, Lambda, and Kinesis.

My expertise extends to data ingestion, cleansing, transformations, and aggregation using tools like Spark SQL, Kafka, Flume, and AWS Glue. I’ve worked extensively on cloud migration, real-time streaming data processing, and optimizing Hive tables for better query performance.

Experienced with designing and optimizing data pipelines to ensure seamless data flow. Utilizes advanced SQL and Python skills to create and maintain robust data architectures. Track record of implementing scalable solutions that enhance data integrity and support informed decision-making.

I have also collaborated with Data Science teams to build machine learning models and developed data pipelines to support these models. Additionally, I’ve led efforts in serverless architecture deployment, managed Databricks workspaces, and implemented Python-based data processing solutions in AWS EMR.

My skills also include experience in data visualization, Google Cloud components, container management with Kubernetes, and a strong foundation in programming languages like Python, Java, and SQL. I’ve been involved in various project life cycles, from design to implementation, using Agile and Waterfall methodologies.

Lastly, I am proficient in maintaining data quality, performing business and data analysis, and ensuring efficient data solutions and client deliverables on time.

Overview

12
12
years of professional experience

Work History

Data Engineer/Big Data Developer

The Bridge Corp
12.2024 - Current
  • Designed end to end scalable architecture to solve business problems using various Azure Components like HDInsight, Data Factory, Data Lake, Storage and Machine Learning Studio
  • Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the SQL Activity
  • Developed Spark applications using Scala and Spark-SQL for data extraction, transformation, and aggregation from multiple file formats for analyzing & transforming the data to uncover insights into the customer usage patterns
  • Experience and Proficiency with Informatica Products & Informatica tools such as PowerCenter, Data Quality, or MDM
  • Worked with designing Informatica ETL mappings, workflows, and transformations
  • Written multiple Hive UDFS using Core Java and OOPS concepts and spark functions within Python programs
  • Written multiple MapReduce programs in Java for data extraction, transformation and aggregation from multiple file formats including XML, JSON, CSV, and other compressed file formats
  • Managed host Kubernetes environment, making it quick and easy to deploy and manage containerized applications without container orchestration expertise
  • Undertake data analysis and collaborated with down-stream analytics team to shape the data according to their requirements
  • Used Azure Event Grid for managing event service that enables you to easily manage events across many different Azure services and applications
  • Delta lake supports mergers, update and delete operations to enable complex use cases
  • Used Azure Data bricks for fast, easy, and collaborative spark-based platform on Azure
  • Used Data bricks to integrate easily with the whole Microsoft stack
  • Wrote spark SQL and spark scripts (Py Spark) in Data bricks environment to validate the monthly account level customer data
  • Environment: Ubuntu 16.04, Hadoop 2.0, Spark (PySpark, Spark streaming, SparkSQL, Spark MLlib), NiFi, Jenkins, Pig 0.15, Python 3.x(Nltk, Pandas), Tableau 10.3, GitHub, Azure (Storage, DW, ADF, ADLS, Databricks), AWS Redshift and Open CV.
  • Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
  • .Fine-tuned query performance and optimized database structures for faster, more accurate data retrieval and reporting.

Data Engineer/Azure Cloud Engineer/Software Engineer

WBA, Walgreens Boots Alliance Via Global Logic INC
03.2024 - 12.2024
  • Assembly-Modernization which involves combination of Rx creation from IC+ application and aligning the App-Manager and Device Emulator for Assembly app and CSOS Application also includes Functional Testing and Automation Testing
  • Role and Responsibilities:
  • Creating Spark clusters and configuring high concurrency clusters using Azure Data bricks (ADB) to speed up the preparation of high-quality data
  • Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in In Azure Data bricks
  • Used Azure Data Catalog which helps in organizing and to get more value from their existing investments
  • Used Azure Synapse to bring these worlds together with a unified experience to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs
  • Utilized the clinical data to generate features to describe the different illnesses by using LDA Topic Modelling
  • Used PCA to reduce dimension and compute eigenvalue and eigenvector and used Open CV
  • Established strong working relationships with clients through exceptional communication skills, fostering trust and collaboration.
  • Wrote, reviewed and edited technical document in accordance with template requirements.

Data Engineer/Developer

Society of actuaries
04.2023 - 02.2024
  • Involved in complete Big Data flow of the application starting from data ingestion upstream to HDFS, processing the data in HDFS and analyzing the data and involved
  • Configured Flume to extract the data from the web server output files to load into HDFS
  • Create external tables with partitions using Hive, AWS Athena, and Redshift
  • Development of Spark structured streaming to read the data from Kafka in real time and batch modes, apply different modes of Change data captures (CDCs) and then load the data into Hive
  • Developing environment, Confidential S3, EC2, Glue, Athena, AWS Data Pipeline, Kinesis streams, Firehouse, Lambda, Redshift, RDS, and Dynamo DB integration
  • Created a React client web-app backed by server less AWS Lambda functions to LINKS Interact with an AWS Sage maker Endpoint
  • Migrate on in-house database to AWS Cloud and designed, built, and deployed a multitude of applications utilizing the AWS stack (Including EC2, RDS) by focusing on high-availability and auto-scaling
  • Involved in designing and deploying multi-tier applications using all the AWS services like (EC2, Route53, S3, RDS, Dynamo DB, SNS, SQS, IAM) focusing on high-availability, fault tolerance, and auto-scaling in AWS Cloud Formation
  • Optimized the performance of an informatica session using techniques like partitioning, indexing, and caching for performance tuning
  • Handled error handling and recovery in Informatica using strategies for managing failed sessions and workflow recovery options
  • Creating Spark clusters and configuring high concurrency clusters using Databricks to speed up the preparation of high-quality data
  • Writing to Glue metadata catalog which in turn enables us to query the refined data from Athena achieving a server less querying environment
  • Worked on goggle cloud platform (GCP) services like compute engine, cloud load balancing, cloud storage, cloud SQL, stack driver monitoring and cloud deployment manager
  • Set up GCP Firewall rules to allow or deny traffic to and from the VM's instances based on specified configuration and used GCP cloud CDN (content delivery network) to deliver content from GCP cache locations drastically improving user experience and latency
  • Implementations of generalized solution models using AWS Sage Maker
  • Extract Real time feed using Kafka and Spark Streaming and convert it to RDD and process data in the form of Data Frame and save the data as Parquet format in HDFS
  • Set up GCP Firewall rules to allow or deny traffic to and from the VM's instances based on specified configuration and used GCP cloud CDN (content delivery network) to deliver content from GCP cache locations drastically improving user experience and latency
  • Development of Spark structured streaming to read the data from Kafka in real time and batch modes, apply different modes of Change data captures (CDCs) and then load the data into Hive
  • Worked with various HDFS file formats like Avro, Sequence File, NiFi, Json and various compression formats like Snappy, bzip2
  • Used Spark-Streaming APIs to perform necessary transformations and actions on the data got from Kafka
  • Process the data from Kafka pipelines from topics and show the real time streaming in dashboards
  • Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data
  • Analyzed the SQL scripts and designed the solution to implement using Scala
  • Used Spark-SQL to Load JSON data and create Schema RDD and loaded it into Hive Tables and handled structured data using Spark SQL
  • Implemented Spark Scripts using Scala, Spark SQL to access hive tables into Spark for faster processing of data
  • Exploring with Spark to improve the performance and optimization of the existing algorithms in Hadoop using Spark context, Spark-SQL, PostgreSQL, Scala, Data Frame, Impala, Open Shift, Talend, pair RDD's
  • Designed columnar families in Cassandra and Ingested data from RDBMS, performed transformations and exported the data to Cassandra
  • Experience on moving raw data between different systems using Apache NIFI
  • Used Elastic search for indexing/full text searching
  • Code and developed a custom Elastic Search java-based wrapper client using the REST API
  • Environment: Hadoop (HDFS, Map Reduce), Databricks, Spark, AWS Services EC2, S3, Glue, Athena, EMR, Redshift, Talend, Impala, Hive, GCP, PostgreSQL, Flink, Jenkins, NiFi, Scala, Mongo DB, Cassandra, Python, Pig, Sqoop, Hibernate, spring, Oozie, Auto scaling, Scala, Azure, Elastic Search, Dynamo DB, UNIX Shell Scripting.
  • Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
  • .Fine-tuned query performance and optimized database structures for faster, more accurate data retrieval and reporting.

Hadoop Spark Developer/Software Developer

AT&T Via Infosys Ltd
05.2020 - 03.2023
  • Project Title: Concord Commission Program for AR Phase and data migration to MS Azure, of the leading Telecommunication Company in USA – AT&T
  • The Project involves the development of Concord Program under which Client pays the commission to their authorized dealers and third-party retailers for selling Client Telecommunication services and products
  • This project involves the implementation of new big data technologies in current architecture to improve and enhance it with respect to customers and dealers for Client and its Authorized Retailers
  • As a part of development through various channels in Client business logic going to be added according to the requirement for the performance improvement and Digital Experience
  • Developed Commission steaming systems for ATT DTV products to focus on the capital spending aspects of multi-million-dollar projects and steaming automation product using Azure/HIVE and Spark/Scala/Databricks environment
  • Payments, adjustments and reconciled them against weekly, monthly actuals for a multi-million-dollar commission budget provided to ATT vendors
  • Role and Responsibilities:
  • Collaborated with Business Analysts, SMEs across departments gather business requirements, and identify workable items for further development
  • Partnered with ETL developers to ensure that data is well cleaned, and the data warehouse is up to date for reporting purposes by Pig
  • Selected and generated data into CSV files and stored them into AWS S3 by using AWS EC2 and then structured and stored in AWS Redshift
  • Involved in requirement analysis, ETL design, and development for extracting data from source systems such as Mainframe, DB2, Sybase, Oracle, and flat files, and putting it into Netezza
  • Responsible for identifying bottlenecks and resolving them using Netezza Database performance tuning
  • Netezza SQL scripts were written to verify that the table was properly loaded
  • Import & Export of data from one server to other servers using tools like Data Transformation Services (DTS)
  • Designed an object detection program by utilizing Python, YOLO model, and TensorFlow to identify objects by drawing the boundaries of each identified object in an image
  • Processed some simple statistical analysis of data profiling like cancel rate, var, skew, Kurt of trades, and runs of each stock everyday group by 1 min, 5 min, and 15 min
  • Used PySpark and Pandas to calculate the moving average and RSI score of the stocks and generated them into the data warehouse
  • Created Stored Procedures, Triggers, Indexes, User defined Functions, Constraints etc on various database objects to obtain the required results
  • Involved in integration of Hadoop cluster with spark engine to perform BATCH and GRAPHX operations
  • Performed data preprocessing and feature engineering for further predictive analytics using Python Pandas
  • Exported Data into Snowflake by creating Staging Tables to load Data of different files from Amazon S3
  • As a part of Data Migration, I wrote many SQL Scripts for Mismatch of data and worked on loading the history data from Teradata SQL to snowflake
  • Developed and validated machine learning models including Ridge and Lasso regression for predicting the total amount of trade
  • Boosted the performance of regression models by applying polynomial transformation and feature selection and using those methods to select stocks
  • Generated report on predictive analytics using Python and Tableau including visualizing model performance and prediction results
  • Utilized Agile and Scrum methodology for team and project management
  • Used Git for version control with colleagues
  • Development and Unit Testing
  • Build and Deployment of Shell Scripts, Spark-Scala Codes and jobs
  • Performed code configurations
  • Automation of Functional Testing using Rational Functional Tester Tool
  • I participated in code and design reviews and also used a check style tool for code quality improvements
  • Coordinated with multiple vendors from geographically distributed teams for successful development, testing and deployment of a single project
  • Worked on pulling the data from CDR DB and loading to LZ and TZ HDFS Locations with Preprocessing using Spark-Scala Code
  • Data File generations in different Formats for SAP Delivery
  • Automation scheduling of jobs using Airflow
  • Worked on Azure data factory to build pipelines and data flow for data migration
  • Designed and built the pipelines to migrate data from on-prem Databases to Azure Data Lake Storage and Azure Synapse Analytics
  • Worked on Azure Key Vault for storing the retrieving the secrets
  • Worked on delta lake for loading the delta tables in Azure Data Bricks
  • Integrated Azure Databricks into Azure data factory pipelines
  • Worked on Azure DevOps CI/CD pipelines for deployment for code
  • Involved in migration of on premise to Azure
  • Built cloud platform strategy unified platform to implement on Azure using Databricks
  • Experienced in Spark, Scala, hive, hdfs, and Unit testing through IntelliJ
  • Setup Airflow job for scheduling
  • Used Sqoop to pull data from various source
  • Involved in performance tuning/fixes in Spark SQL
  • Developed UNIX Shell script to sync up data from Cloud to HDFS
  • Developed and migrated existing Python code to Scala
  • Involved Data Analytics and production support
  • Environment: Spark (PySpark, SparkSQL, SparkMLIib), Python 3.x (Scikit-learn), Kafka, Spark, Scala, HDFS, Hive, Map Reduce, SOLR, Impala, Oracle, Sqoop, SQL Talend, Python, Yarn, Pig, Oozie, Tableau, Maven, Jenkins, Cloudera, JUnit, agile methodologies
  • Optimized application performance by profiling Spark jobs and identifying bottlenecks in code execution.
  • Developed reusable libraries and tools to expedite development cycles and improve code quality in Spark applications.

Hadoop Spark Developer/Software Developer

Johnson Controls Via Infosys Ltd
12.2019 - 04.2020
  • Project Title: Data Migration Project using Attunity Replicate & Compose ETL
  • Client: One of the leading Manufacturing Company in USA -Johnson Controls
  • The project is about migrating data from various data sources like Oracle, SQL server to ADLS for storing and analyzing information with some Global Transformations
  • The challenges are configuration of Attunity Replicate and compose and solving Log errors
  • Role and Responsibilities:
  • Configuration of Attunity Replicate for sources like Oracle, SQL Server and setup the ETL pipeline flow
  • Created some Global Transformations for inserting column and timestamps in required tables
  • Defining standard naming conventions for different parameters like sources, targets, Jobs, Storage, Databases
  • Created databases in Hive for storage and staging purposes
  • Created workflows and storage configuration for scheduling of jobs in Attunity Compose
  • Migrated data from Oracle, MySQL using Attunity ETL into Azure Data Lake Storage (ADLS)
  • Developed Global Transformations in Attunity Replicate and designed pipelines, Storage Configuration and workflows in Attunity Compose
  • Worked with Hive staging tables and databases for data handling.
  • The project is about migrating data from various data sources like Oracle, SQL server to ADLS for storing and analyzing information with some Global Transformations
  • The challenges are configuration of Attunity Replicate and compose and solving Log errors
  • Role and Responsibilities:
  • Configuration of Attunity Replicate for sources like Oracle, SQL Server and setup the ETL pipeline flow
  • Created some Global Transformations for inserting column and timestamps in required tables
  • Defining standard naming conventions for different parameters like sources, targets, Jobs, Storage, Databases
  • Created databases in Hive for storage and staging purposes
  • Created workflows and storage configuration for scheduling of jobs in Attunity Compose
  • Migrated data from Oracle, MySQL using Attunity ETL into Azure Data Lake Storage (ADLS)
  • Developed Global Transformations in Attunity Replicate and designed pipelines, Storage Configuration and workflows in Attunity Compose
  • Worked with Hive staging tables and databases for data handling.
  • Optimized application performance by profiling Spark jobs and identifying bottlenecks in code execution.
  • Developed reusable libraries and tools to expedite development cycles and improve code quality in Spark applications.

Software engineer/Developer

Scientific Games
11.2017 - 12.2019
    • As a Data Engineer, I designed and deployed scalable, highly available, and fault tolerant systems on Azure
    • Involved in complete SDLC life cycle of big data project that includes requirement analysis, design, coding, testing and production
    • Lead the estimation, review the estimates, identify the complexities and communicate to all the stakeholders
    • Defined the business objectives comprehensively through discussions with business stakeholders, functional analysts and participating in requirement collection sessions
    • Migrated on-primes environment on Cloud using MS Azure
    • Performed data Ingestion for the incoming web feeds into the Data Lake store which includes both structured and unstructured data
    • Designed the business requirement collection approach based on the project scope and SDLC (Agile) methodology
    • Migrated data warehouses to Snowflake Data warehouse
    • Installed and configured Hive and written Hive UDFs and Cluster coordination services through Zookeeper
    • Installed and configured Hadoop Ecosystem components
    • Worked with data ingestions from multiple sources into the Azure SQL data warehouse
    • Transformed and loading data into Azure SQL Database
    • Wrote Spark applications for Data validation, cleansing, transformations and custom aggregations
    • Developed HIVE scripts to transfer data from and to HDFS
    • Implemented Hadoop based data warehouses, integrated Hadoop with Enterprise Data Warehouse systems
    • Performed reverse engineering using Erwin to redefine entities, attributes and relationships existing database
    • Development and maintenance of data pipelines on Azure Analytics platform using Azure Databricks
    • Created Airflow Scheduling scripts in Python
    • Ingested data into HDFS using Sqoop and scheduled an incremental load to HDFS
    • Worked with Hadoop infrastructure to storage data in HDFS storage and use HIVE SQL to migrate underlying SQL codebase in Azure
    • Created Data Pipeline to migrate data from Azure Blob Storage to Snowflake
    • Worked on Snowflake modeling and highly proficient in data warehousing techniques for data cleansing, Slowly Changing Dimension phenomenon, surrogate key assignment and change data capture
    • Maintained the NoSQL database to handle unstructured data, clean the data by removing invalidate data, unifying the format and rearranging the structure and load for following steps
    • Participated in NoSQL database maintaining with Azure Sql DB
    • Involved in Kafka and building use case relevant to our environment
    • Identified data within different data stores, such as tables, files, folders, and documents to create a dataset in pipeline using Azure HDInsight
    • Optimized and updated UML Models (Visio) and Relational Data Models for various applications
    • Wrote Python scripts to parse XML documents and load the data in database
    • Written DDL and DML statements for creating, altering tables and converting characters into numeric values
    • Translated business concepts into XML vocabulary by designing XML Schemas with UML
    • Worked on Data load using Azure Data factory using external table approach
    • Automated recurring reports using SQL and Python and visualized them on BI platform like Power BI
    • Designed and generated various dashboards, reports using various Power BI Visualizations
    • Implemented end-to-end systems for Data Analytics, Data Automation and integrated with custom visualization tools
    • Developed purging scripts and routines to purge data on Azure SQL Server and Azure Blob storage
    • Developed Python Scripts for automation purposes and Component unit testing using Azure Emulator
    • Involved in T-SQL queries and optimizing the queries in SQL Server
    • Maintaining data storage in Azure Data Lake
    • Environment: Hadoop 3.0, Hive, Zookeeper, Erwin 9.8, SQL, PL/SQL, Agile, Snowflake, Azure Data Lake, Azure Data factory, MDM, XML, Azure Databricks, T-SQL.
    • Improved software functionality by identifying and resolving various coding issues.
    • Refactored legacy systems for enhanced maintainability, allowing for easier upgrades and modifications as needed over time.

Big Data Developer

Gem Sonics Private Limited, TXM Corp
04.2013 - 07.2016
  • Developed proof of concept (POC) for real data ingestion using Kafka, Storm, zookeeper, HBase
  • Developed Pig Latin Scripts to extract the data from the web server and the output files to load into HDFS
  • Worked on migrating Map Reduce programs into Spark transformations using Spark and Scala, initially done using python
  • Developed Spark jobs using Scala on top of Yarn/MRv2 for interactive and Batch analysis
  • Experienced in querying data using Spark SQL on top of Spark engine for faster data sets processing
  • Worked on implementing Spark Framework to implement transformations on data
  • Used Pig as ETL tool to do Transformations, even joins and some pre-aggregations before storing the data into HDFS
  • Expert knowledge on Cosmos DB NoSQL data modeling, tuning, disaster recovery backup used for distributed storage and processing using CRUD
  • Extracted and restructured the data into Cosmos DB using import and export command line tool
  • Implemented Custom Sterilizer, interceptors to Mask, created confidential data and filter unwanted records from the event payload in flume
  • Wrote Flume configuration files for importing streaming log data into HBase with Flume
  • Imported several transaction logs from web servers with Flume to ingest the data into HDFS using Flume and Spool directory for loading the data from local system (LFS) to HDFS
  • Environment: Kafka, Spark, Scala, HDFS, Hive, Map Reduce, SOLR, Impala, Oracle, Sqoop, SQL Talend, Python, Yarn, Pig, Oozie, Tableau, Maven, Jenkins, Cloudera, JUnit, agile methodologies.
  • Enhanced data processing efficiency by designing and implementing big data solutions using Hadoop ecosystem tools.
  • Delivered actionable insights from massive datasets using advanced machine learning algorithms implemented in Python or Scala programming languages.

Education

Master of Science - Computer Science

Chicago State University
Chicago, IL
06-2019

Bachelor of Science -

Kurukshetra University
India
08-2013

Skills

  • ETL development
  • Data warehousing
  • Data modeling
  • Data pipeline design
  • Data migration
  • Big data processing
  • Scripting languages
  • Spark framework
  • Performance tuning
  • SQL expertise
  • Data governance
  • Real-time analytics
  • NoSQL databases
  • API development
  • Data quality assurance
  • Hadoop ecosystem
  • Metadata management
  • Data integration
  • SQL and databases
  • SQL programming
  • Business intelligence
  • Data analysis
  • Storage virtualization
  • Big data technologies
  • Data mining
  • Problem-solving
  • Teamwork and collaboration
  • Time management
  • Report generation
  • Active listening
  • Critical thinking
  • Task prioritization
  • Problem-solving abilities
  • Team collaboration
  • Python programming
  • Interpersonal skills
  • Data quality management
  • Database optimization
  • ETL processes
  • Software development
  • Data visualization
  • Infrastructure planning
  • Java programming
  • Problem-solving aptitude
  • Machine learning
  • Data security
  • Data pipeline control
  • Proficiency in Python
  • Database design
  • Relational databases
  • Advanced analytics
  • Data analytics
  • Data warehousing expertise

Technical Skillset

Big Data/Hadoop Technologies - MapReduce, Spark, Spark SQL, Azure, Spark Streaming, Kafka, PySpark, Pig, Hive, HBase, Flume, Flink , Yarn, Oozie, Zookeeper, Hue, Ambari Server, Teradata, GCP,NIFI,
Languages - HTML5, DHTML, WSDL, CSS3, C, C++, XML, R/R Studio, SAS Enterprise Guide, SAS, R (Caret, Weka, ggplot), Perl, MATLAB, Mathematica, FORTRAN, DTD, Schemas, Json, Ajax, Java, Scala, Python (NumPy, SciPy, Pandas, Genism, Kera’s), Java Script, Shell Scripting
NO SQL Databases - Cassandra, HBase, MongoDB, Maria DB
Web Design Tools - HTML, CSS, JavaScript, JSP, jQuery, XML
Development Tools - Microsoft SQL Studio, IntelliJ, Azure Databricks, Eclipse, NetBeans.
Public Cloud - EC2, IAM, S3, Auto scaling, Cloud Watch, Route53, EMR, RedShift, Glue, Athena, Sage Maker.
Orchestration tools - Oozie, Airflow.
Development Methodologies - Agile/Scrum, UML, Design Patterns, Waterfall
Build Tools - Jenkins, Toad, SQL Loader, PostgreSQL, Talend, Maven, ANT, RTC, RSA, Control-M, Oozie, Hue, SOAP UI
Reporting Tools - MS Office (Word/Excel/Power Point/ Visio/Outlook), Crystal reports XI, SSRS, Cognos.
Databases - Microsoft SQL Server 2008,2010/2012, MySQL 4.x/5.x, Oracle 11g, 12c, DB2, Teradata, Netezza
Operating Systems - All versions of Windows, UNIX, LINUX, Macintosh HD, Sun Solaris

Languages

English
Full Professional

Timeline

Data Engineer/Big Data Developer

The Bridge Corp
12.2024 - Current

Data Engineer/Azure Cloud Engineer/Software Engineer

WBA, Walgreens Boots Alliance Via Global Logic INC
03.2024 - 12.2024

Data Engineer/Developer

Society of actuaries
04.2023 - 02.2024

Hadoop Spark Developer/Software Developer

AT&T Via Infosys Ltd
05.2020 - 03.2023

Hadoop Spark Developer/Software Developer

Johnson Controls Via Infosys Ltd
12.2019 - 04.2020

Software engineer/Developer

Scientific Games
11.2017 - 12.2019

Big Data Developer

Gem Sonics Private Limited, TXM Corp
04.2013 - 07.2016

Master of Science - Computer Science

Chicago State University

Bachelor of Science -

Kurukshetra University

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