SUMMARY

• 7+ years of experience as a Hadoop/Bid Data Developer involved in deploying, maintaining, monitoring and upgrading Hadoop Clusters using Apache, Cloudera (CDH), Hortonworks Distributions.
• Expert in Hadoop Ecosystem including HDFS, MapReduce, Hive, Pig, Kafka, YARN, Spark, HBase, Zookeeper, Oozie, Zookeeper, Flume and Sqoop based Big Data Platforms.
• Working experience in using Apache Hadoop ecosystem components like Map Reduce, HDFS, Hive, Sqoop, Pig, Oozie, Flume, HBase, and Zoo Keeper.
• Strong experience in data analytics using Hive and Pig, including by writing custom UDFs.
• Performed Importing and exporting data into HDFS and Hive using Sqoop.
• Knowledge of job workflow scheduling and monitoring tools like Oozie and Zookeeper.
• Knowledge of creating Map Reduce codes in Java as per the business requirements.
• Experience in importing and exporting streaming data into HDFS using stream processing platforms like Flume and Kafka.
• Worked on Java EE 7 and 8. Developed ETL\Hadoop related java codes, created RESTful APIs using Spring Boot Framework, developed web apps using Spring MVC and JavaScript, developed coding framework, etc.
• Experience in developing spring Boot applications for transformations.
• Hands - on experience on configuring a Hadoop cluster for a production environment, Amazon Web Services (AWS) using an EC2 instance.
• Sound financial domain knowledge of Fixed Income, Bonds, Equities, Trade Cycle, Derivatives (Options and Futures), Portfolio Management, Sales and Marketing, CCAR and risk management.
• Expertise in using ETL tool Informatica to Extract, Transform and Load the data into warehouse.
• Extensively used ETL methodology for performing Data Profiling, Data Migration, Extraction.
• Extensive knowledge in using SQL Queries for backend database analysis.
• Good Experience in developing applications using JavaJ2EE technologies includes Servlets, Struts, JSP, and JDBC.
• Strong experience working with AWS Cloud services like S3, EMR, Redshift, Athena, Glue metastore etc.
• Having good experience on using Tableau, Qlik view Reporting Tools.
• Experienced using Redshift warehouse with large amount of data, some ETL related work using Glue.
• Experienced in implementing scheduler using Oozie, Airflow, Crontab and Shell scripts.
• Experience on Java8, Scala and Play/Akka framework.
• Experience in monitoring Hadoop clusters on VM, Horton Works Data Platform 2.1 2.2, 2.4, 2.6, and 3.0, CDH5 Cloudera Manager, HDP on Linux.
• Profound experience in creating real time data streaming solutions using Apache Spark/Spark Streaming, Kafka.
• Extensive experience in architecting, loading and analyzing large datasets with Hadoop framework (HDFS, pig, hive, Flume, Sqoop, spark, Pyspark, NIFI, Scala, slor, storm) and NoSQL databases.
• Good knowledge and Hands-on experience in storing, processing unstructured data using NOSQL databases like HBase and MongoDB.
• Designed the Compliance Data Warehouse using Power Designer- Conceptual, Logical and Physical based on subject area - Equity, Fixed Income, Listed Derivatives, Index, Equity Transactions, Cost Centre, HR Employee data, Asset management; Fix protocol.
• Developed analytical components using Spark and Spark Stream.
• Experience in Oozie and workflow scheduler to manage Hadoop jobs by Direct Acyclic Graph (DAG) of actions with control flows.
• Knowledge on implementing Big Data in Azure Databricks for processing, managing Hadoop framework.
• Worked on a prototype Apache Spark Streaming project, and converted our existing Java Strom Topology.
• Designed and developed logical and physical data models that utilize concepts such as Star Schema, Snowflake Schema and Slowly Changing Dimensions.
• Experienced on Hadoop cluster on Azure HD Insight Platform and deployed Data analytic solutions using tools like Spark and BI reporting tools.
• Expert in installation, configuration, designing and monitoring Open Source Kafka and Confluent Kafka in various environments.
• Experienced with installation and configuration of Kubernetes, clustering and managed local deployments in Kubernetes .
• Used Maven extensively for building MapReduce jar files and deployed it to Amazon Web Services (AWS) using EC2 virtual Servers in the cloud and Experience in build scripts to do continuous integrations systems like Jenkins.
• Knowledge in OLTP/OLAP System Study and E-R modeling, developing Database Schemas like Star schema and Snowflake schema used in relational, dimensional and multidimensional modeling.
• Document (FSD). Experienced on cloud integration with AWS using Elastic Map Reduce (EMR), Simple Storage Service, EC2, Redshift, Hands on experience with the AWS CLI and SDK tools.
• Well-versed in Agile, other SDLC methodologies and can coordinate with owners and SMEs.
• Experienced in creating and analyzing Software Requirement Specifications (SRS) and Functional Specification Document (FSD).
• Strong knowledge of Software Development Life Cycle (SDLC).
• Experienced in preparing and executing Unit Test Plan and Unit Test Cases after software development.
• Worked extensively on Health and Automotive Insurance domains.
• Experienced to work with multi-cultural environment with a team and also individually as per the project requirement.

TECHNICAL SKILLS

Big Data Technologies: HDFS, Hive, SAS, MapReduce, Pig, Apache Spark, Sqoop, Oozie, Flume, HDP 2.2, 2.4, 2.6, 3.0, CDH 5.x, Kafka, Apache Airflow, YARN and Spark

Scripting Languages: Shell, PySpark, JavaScript, Python

ETL Tools: Confidential Data Stage version 8.1,8.7,11.3,11.5,11.7, Confidential Data Integrator 11g/12c (ODI), Informatica Powercenter 9/10.1

Programming Languages: Java, Scala, Python, SQL, C

Cloud Technologies: Amazon EC2, S3, EMR, Dynamo DB, Lambda, Kinesis, ELB, RDS, Glue, SNS, SQS, EBS, CloudFormation

Hadoop Distributions: Cloudera, Hortonworks, MapR

NoSQL databases: HBase, Mongo DB, Cassandra

Other Tools: J-Unit, Log4j, Rational Clear Case, SVN, GitHub, Jenkins, Tableau ANT, Maven, JBuilder, AWS, Azure

IDE: Eclipse, NetBeans

Build Tools: Maven, Ant

Operating systems: UNIX, LINUX, Mac OS and Windows

Databases: Confidential, SQL Server, MySQL.

PROFESSIONAL EXPERIENCE

Confidential, Chattanooga, TN

Big-Data/Hadoop Developer

Responsibilities:

• Expertise in designing and deployment of Hadoop cluster and different Big Data analytic tools including Pig, Hive, HBase, Oozie, ZooKeeper, SQOOP, flume, Spark, Cassandra with Hortonworks and Cloudera.
• Installed Hadoop, Map Reduce, HDFS, Azure, and developed multiple MapReduce jobs in PIG and Hive for data cleaning and pre-processing.
• Programming in Hive, writing the scope scripts in Data Lake and HDFS to structure the Peta bytes of unstructured data stored in the Azure DataLake (Cosmos) big data system.
• Job duties include design and development of various modules in Hadoop Big Data platform and processing data using MapReduce, Hive, SQOOP, Kafka and Oozie.
• Understanding business needs, analyzing functional specifications and map those to develop and designing MapReduce programs and algorithms.
• Used AWS Data Pipeline to schedule an Amazon EMR cluster to clean and process web server logs stored in Amazon S3 bucket.
• Replaced existing MapReduce jobs and Hive scripts with Spark Data-Frame transformation and actions for the faster analysis of the data.
• Created the required airflow Dags by using the various airflow operators necessary in orchestrating the workflow.
• Plan, develop, and apply leading-edge analytic and quantitative tools and modeling techniques to help clients gain insights and improve decision-making.
• Implemented Visualization of Dashboard from R analytical data to Tableau.
• Used Reporting tools like Tableau to connect with Hive for generating daily reports of data.
• Developed NIFI pipeline using ELK for quicker testing and handling of information.
• Written Pig and Hive jobs to parse the logs and structure them in tabular format to facilitate effective querying on the log data. Also have hand on Experience on Pig and Hive User Define Functions (UFD).
• Execution of Hadoop ecosystem and Applications through Apache HUE.
• Developed a job server (REST API, spring boot, Confidential DB) and job shell for job submission, job profile storage, job data (HDFS) query/monitoring.
• Optimizing Hadoop MapReduce code, Hive/Pig scripts for better scalability, reliability and performance.
• Developed the OOZIE workflows for the Application execution.
• Feasibility Analysis (For the deliverables) - Evaluating the feasibility of the requirements against complexity and time lines.
• Read variety of databases from Azure Data Bricks using JDBC connections using Scala and Pyspark and saved in ADL.
• Written pyspark job in AWS Glue to merge data from multiple table.
• Utilized Crawler to populate AWS Glue data Catalog with metadata table definitions.
• Generated a script in AWS Glue to transfer the data.
• Developed data pipelines for ingress and egress of data into and from the Data Lake using various tools (PySpark, SQOOP, COPY).
• Implemented the workflows using Airflow scheduler to automate tasks.
• Developed Microservices based on Restful web service using Akka Actors and Akka-Http framework in Scala which handles high concurrency and high volume of traffic.
• Involved in deploying our Microservices on Docker containers and created Kubernetes clusters for Understanding of OLAP, OLTP, Business Intelligence and Data Warehousing concepts with emphasis on ETL and Business Reporting needs.
• Worked on Go-cd (ci/cd tool) to deploy application and have experience with Munin frame work for BigData Testing.
• Developed bash scripts to bring the T-Log files from ftp server and then processing it to load into Hive tables.
• Worked on AWS Glue for scheduling jobs and Automated Glue with CloudWatch events.
• Worked with Kafka streaming tool to load data into HDFS and exported it into MongoDB.
• Enable and configure Hadoop services such as HDFS, YARN, Hive, Ranger, Hbase, Kafka, Sqoop, Zeppeline Notebook and Spark/Spark2 and involved in analyzing log data to predict the errors by using Apache Spark.
• Programming in Hive, writing the scope scripts in Data Lake and HDFS to structure the Peta bytes of unstructured data stored in the Azure DataLake (Cosmos) big data system.
• Used Pig as ETL tool to do transformations, event joins and some pre-aggregations before storing the data on to HDFS.
• Developing predictive analytic using Apache Spark Scala APIs.
• Uploaded and processed terabytes of data from various structured and unstructured sources into HDFS (AWS cloud) using Sqoop and Flume.
• Creating PPT presentation to show case the flow of project.
• Used maven to build and deploy the Jars for MapReduce, Pig and Hive UDFs.
• Responsible for fine-tuning PySpark applications/jobs to improve the efficiency and overall processing time for the pipelines.
• Used chef, Terraform as Infrastructure as code (IaaS) for defining Jenkins plugins.
• Created solutions to transform data from various sources and load it into platforms such as Hadoop, Snowflake to create a data lake.
• Implemented AWS Lambda functions to run scripts in response to events in Amazon Dynamo DB table or S3 bucket or to HTTP requests using Amazon API Gateway.
• Used Open Shift application server for deploying and configuring application.
• Involved in analyzing data coming from various sources and creating Meta-files and control files to ingest the data in to the Data Lake.
• Involved in configuring batch job to perform ingestion of the source files in to the Data Lake and developed Pig queries to load data to HBase
• Implementing Data warehouse solutions in Amazon web services (AWS) Redshift; Worked on various projects to migrate data from on premise databases to AWS Redshift, RDS and S3.
• Worked on Building and implementing real-time streaming ETL pipeline using Kafka Streams API.
• Created a Spark application to process and stream data from Kafka to MySQL.
• Performing data migration from Legacy Databases RDBMS to HDFS using Sqoop.
• Writing Pig scripts for data processing.
• Programmed in Hive, Spark SQL and Python to streamline the incoming data and build the data pipelines to get the useful insights, and orchestrated pipelines using Azure Data Factory.
• Used ORC, Parquet file formats on HDInsight, Azure Blobs and Azure tables to store for raw data.
• Implemented Hive tables and HQL Queries for the reports. Written and used complex data type in Hive. Storing and retrieved data using HQL in Hive. Developed Hive queries to analyze reducer output data.
• Highly involved in designing the next generation data architecture for the unstructured data.
• Developed PIG Latin scripts to extract data from source system.
• Involved in Extracting, loading Data from Hive to Load an RDBMS using Sqoop.
• Designed, documented operational problems by following standards and procedures using a software reporting tool JIRA.

Environment: CDH4, HDFS, Map Reduce, Hive, Oozie, Java, Amazon EMR, Amazon S3, Amazon Glue, CloudWatch, Amazon Kinesis,, PIG, Shell Scripting, Maven, HDP 2.6, Linux, HUE, Airflow, Sqoop, Akka, Tableau, Flume, Snowflake, NoSQL, DB2, and Confidential .

Confidential, Santa Clara, CA

Big-Data/Hadoop developer

Responsibilities:

• Responsible for building scalable distributed data solutions using Hadoop.
• Responsible for cluster maintenance, adding and removing cluster nodes, cluster monitoring and troubleshooting, managing and reviewing data backups and Hadoop log files.
• Installed, configured and maintained Hortonworks HDP 2.2 using Ambari and manually through command line.
• Designed, development, and implementation of performant ETL pipelines using python API (pySpark) of Apache Spark on Azure Databricks.
• Worked on designing and developing dashboards using Tableau and Qlikview for visualizing the data and presented dashboards to higher-level hierarchy.

  Communicated with BI team when building the dashboards in Tableau, Qlikview and Qliksense.

• Worked with Big data tools like Pig, Hive, Spark, and worked with data engineers on deployment tools like Flask, Kubernetes and Docker for validating and maintaining model performance.
• Worked extensively with Flume for importing social media data.
• Involved in converting Hive/SQL queries into Spark transformations using Spark RDDs, Python and Scala.
• Continuous monitoring and managing the Hadoop cluster through Cloudera Manager.
• Analyzed the data by performing Hive queries and running Pig scripts to know user behavior.
• Installed Oozie workflow engine to run multiple Hive and Pig jobs.
• Created a Kafka broker which uses schema to fetch structured data in structured streaming.
• Spark streaming to receive real time data using Kafka.
• Attended workshops and sessions from Cloud era, GitHub, Qlik, Tableau and Micro Strategy.
• Created POC on Hortonworks and suggested the best practice in terms HDP, HDF platform
• Developed Flume ETL job for handling data from HTTP Source and Sink as HDFS.
• Handled importing of data from various data sources using Sqoop, performed transformations using Hive, MapReduce, loaded data into HDFS.
• Developed data warehouse model in snowflake for over 100 datasets using whereScape.

  Heavily involved in testing Snowflake to understand best possible way to use the cloud resources.

• Develop Python and bash scripts for automation and implemented Map Reduce jobs using Java API and Python using Spark.
• Build data visualization dashboards in tools like Tableau, QuickSight, and Splunk.
• Migrated ETL jobs to Pig scripts do Transformations, even joins and some pre-aggregations before storing the data onto HDFS.
• Created functions in Lambda that aggregates the data from incoming events, then stores resulting data in Amazon Dynamo DB and S3.
• Used the AWS-CLI to suspend an AWS Lambda function processing an Amazon Kinesis stream, then to resume it again.
• Migrated ETL(NiFi, Glue) jobs to Pig scripts do Transformations, even joins and some pre-aggregations before storing the data onto HDFS.
• Developed ELT workflows using NiFI to load data into Hive and Teradata.
• Extensively used Stash Git-Bucket for Code Control and Worked on AWS Components such as Airflow, Elastic Map Reduce (EMR), Athena and Snow-Flake.
• Identified also alternatives from connect direct data transfers and the use of sqoop, flume, pyspark, spark, spark streaming, kafka and NIFI.
• Worked on Migrating jobs from NiFi development to Pre-PROD and Production cluster.
• Scheduled different Snowflake jobs using NiFi.
• Automated the installation of ELK agent (file beat) with Ansible playbook. Developed KAFKA Queue System to Collect Log data without Data Loss and Publish to various Sources.
• Extracted actionable insight of data using AWS Glue, after careful evaluation and testing, we concluded that AWS glue ETL jobs meet all our needs and free us for procuring and maintaining infrastructure.
• Used Spark API over Cloudera Hadoop YARN to perform analytics on data in Hive.
• Developed data pipeline using Spark, Hive and HBase to ingest customer behavioral data and financial histories into Hadoop cluster for analysis.
• Scheduled Clusters with Cloud Watch and created Lambdas to generate operational alerts for various workflows.
• Used NiFi to ping snowflake to keep Client Session alive.
• Apply various machine learning algorithms and statistical modeling like regression models, text analytics, natural language processing (NLP), supervised and unsupervised, regression models, Random forests regression.
• Analyzed the SQL scripts to design and develop the solution to implement in PySpark.
• Migrated data from on-prem Netezza data warehouse to Cloud AWS S3 buckets using data pipelines written PySpark.
• Created and altered HBase tables on top of data residing in Data Lake and Created external Hive tables on the Blobs to showcase the data to the Hive Meta Store.
• Designed rich data visualization to model data into readable form with Tableau & Matplotlib.
• Documented logical, physical, relational and dimensional data models. Designed the Data Marts in dimensional data modeling using star and snowflake schemas.
• Deployed model using RESTful APIs and used Dockers to facilitate multi-environment transition.
• Processed multiple terabytes of data stored in AWS using Elastic Map Reduce (EMR) to AWS Redshift.
• Implemented security measures AWS provides, employing key concepts of AWS Identity and Access Management (IAM).
• Worked on micro services architecture in spring Boot integrating with various restful web services.
• Involved in deploying our Micro services on Docker containers and created Kubernetes clusters for reliability and scalability of the Micro services.
• Used Spark Data Frames API over Cloudera platform to perform analytics on Hive data.
• Installed, Configured and Managed AWS Tools such as ELK, Cloud Watch for Resource Monitoring.
• Provided consistent environment using Kubernetes for deployment scaling and load balancing to the application from development through production, easing the code development and deployment pipeline by implementing Docker containerization.
• Worked on Hive joins (MEGAJOIN) to produce the input data set to the Qlikview model.
• Configured Sqoop and developed scripts to extract data from MySQL into HDFS.
• Hands-on experience with productionalizing Hadoop applications viz. administration, configuration management, monitoring, debugging and performance tuning.
• Created HBase tables to store various data formats of PII data coming from different portfolios.
• Processing of streaming data using STORM.
• Cluster co-ordination services through ZooKeeper.

Environment: Hadoop, MapReduce, HDFS, Hive, Java, SQL, Cloudera Manager, Hortonworks (HDP 2.2), Pig, Sqoop, Oozie, ZooKeeper, PL/SQL, MySQL, NoSQL, Tableau,Qlik, Airflow, Linux, Windows, Snowflake, Oozie, HBase, STORM

Confidential, Atlanta, GA

Hadoop developer

Responsibilities:

• Installed and configured Hadoop MapReduce, HDFS and developed multiple MapReduce jobs in Java for data cleansing and preprocessing.
• Importing and exporting data into HDFS and Hive using Sqoop.
• Designed and deployed data pipelines using DataLake, DataBricks, and Apache Airflow.
• Used Multithreading, synchronization, caching and memory management.
• Proactively monitored systems and services, architecture design and implementation of Hadoop deployment, configuration management, backup, and di Tableauter recovery systems and procedures.
• Extracted files from MongoDB through Sqoop and placed in HDFS and processed.
• Used Flume to collect, aggregate, and store the web log data from different sources like web servers, mobile and network devices and pushed to HDFS.
• Develop Shell scripts to perform various ETL jobs like creating staging and final tables.
• Developed Spark Applications by using Scala and Implemented Apache Spark data processing project to handle data from various RDBMS and Streaming sources.
• Worked with to create external tables, staging tables and joined the tables as per the requirement and built multiple data pipelines.
• Operated the cluster on AWS by using EC2, EMR, S3, LAMBDA and Elastic Search.
• Worked with Kafka for Proof of Concept to carry out log processing on distributed system.
• Involved in defining job flows using Oozie for scheduling jobs.
• Created Reports with different Selection Criteria from Hive Tables on the data residing in Data Lake.
• Worked on Kafka to import real-time weblogs and ingested the data to Spark Streaming.
• Developed business logic using Kafka Direct Stream in Spark Streaming and implemented business transformations.
• Ingested data into Azure Blob storage and processed the data using Databricks. Involved in writing Spark Scala scripts and UDF's to perform transformations on large datasets.
• Implemented PySpark RDD transformations to map business analysis and apply actions on top of transformations.
• Created Active Batch jobs to automate the Pyspark and SQL functions as daily run jobs.
• Have used Ansible for automation of frameworks.
• Involved in deploying our Micro services on Docker containers and created Kubernetes clusters for reliability.
• Created alarms in Cloud Watch service for monitoring and VPC with Subnets, Lambda provisioning, CPU Utilization and maintained virtual servers on EC2.
• Installation of docker and setting up of kubernetes cluster.
• Installed, Configured and Managed AWS Tools such as ELK, Cloud Watch for Resource Monitoring.
• Implemented Spark using Scala and utilizing Data frames and Spark SQL API for faster processing of data.
• Load and transform large sets of structured, semi structured and unstructured data.
• Supported Map Reduce Programs those are running on the cluster.
• Wrote shell scripts to monitor the health check of Hadoop daemon services and respond accordingly to any warning or failure conditions.
• Involved in loading data from UNIX file system to HDFS, configuring Hive and writing Hive UDFs.
• Utilized Java and MySQL from day to day to debug and fix issues with client processes
• Managed and reviewed log files.
• Implemented partitioning, dynamic partitions and buckets in HIVE.

Environment: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, CouchDB, Flume, HTML, XML, Tableau, Snowflake, SQL, MySQL, J2EE,Linux, Eclipse

Confidential, Houston, TX

Hadoop Developer

Responsibilities:

• Participated in application planning, design activities by interacting and collecting requirements from the end users.
• Gathered the business requirements from the Business Partners and Subject Matter Experts.
• Installed and configured Hadoop, MapReduce, and HDFS clusters.
• Created Hive tables, loaded the data and Performed data manipulations using Hive queries in MapReduce Execution Mode.
• Developed MapReduce programs to cleanse the data in HDFS obtained from heterogeneous data sources to make it suitable for ingestion into Hive schema for analysis.
• Developed Spark SQL scripts using Scala to perform transformations and actions on RDD’s in spark for faster data Processing.
• Experience of performance tuning Hive ETL Scripts, Pig Scripts, MR Jobs in production environment by altering job parameters.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Snowflake and AWS big data technologies
• Loaded the structured data which was resulted from MapReduce jobs into Hive tables.
• Identified issues on behavioral patterns and analyzed the logs using Hive queries.
• Analyzed and transformed stored data by writing MapReduce or Pig jobs based on business requirements.
• Involved in installing, configuring and managing Hadoop Ecosystem components like Hive, Pig, Sqoop, Kafka and Flume.
• Leveraged spark to manipulate unstructured data and apply text mining on user's table utilization data.
• Involved in converting Hive/SQL queries into Spark transformations using Spark RDDs, Python and Scala.
• Used Flume to collect, aggregate, and store the weblog data from different sources like web servers, mobile, and network devices and import to HDFS
• Using Oozie, developed a workflow to automate the tasks of loading the data into HDFS and pre-processing with Pig scripts.
• Created SQL queries and Stored Procedures for CRUD (Create, Read, Update and Delete) operations on database.
• Involved in using Spark DataFrames to create Various Datasets and applied business transformations and data cleansing operations using DataBricks Notebooks.
• Involved in deploying our Micro services on Docker containers and created Kubernetes clusters for reliability and scalability of the Micro services.
• Integrated Map-Reduce with HBase to import bulk data using MR programs
• Used Maven for building jar files of MapReduce programs and deployed to Cluster.
• Utilized AWS Cloud Watch to monitor the performance environment instances for operational and performance metrics during load testing.
• Used Grid Search to evaluate each model and to find best hyper-parameters for each model.
• Designed and implemented a recommendation system which utilized Collaborative filtering techniques to recommend course for different customers and deployed to AWS EMR cluster.
• Build Kibana dashboards to monitor health of Elastic Search Cluster from time to time basis.
• Developed RESTful APIs using Spring Boot Framework that included Gradle as per the business requirement.
• Used cross-validation to test the models with different batches of data to optimize the models and prevent overfitting.

Environment: HDFS, Map Reduce, Pig, Hive, Oozie, Sqoop, Spark, Flume, HBase, Talend, MySQL, Hive QL, NoSQL, PL/SQL, Java, Python, Maven, NoSQL, Avro, Eclipse and Shell Scripting.

Confidential, Hilmar, CA

Java/ J2EE developer

Responsibilities:

• Responsible for understanding the scope of the project and requirement gathering.
• Review and analyze the design and implementation of software components/applications and outline the development process strategies
• Coordinate with Project managers, Development and QA teams during the course of the project.
• Used Spring JDBC to write some DAO classes to interact with the database to access account information.
• Using Spring Framework, Axis, developed web services including design of the XML request/response structure.
• Implemented Hibernate/Spring framework for Database and business layer.
• Configured Confidential with Hibernate, wrote hibernate mapping and configuration files for database processing (Create, Update, select) operations.
• Used Tomcat web server for development purpose.
• Used Confidential as Database and used Toad for queries execution and also involved in writing SQL scripts, PL/SQL code for procedures and functions.
• Used CVS, Perforce as configuration management tool for code versioning and release.
• Developed application using Eclipse and used build and deploy tool as Maven.
• Used Log4J to print the logging, debugging, warning, info on the server console.
• Extensively used Core Java, Servlets, JSP and XML
• Involved in configuring and deploying of code to different environments Integration, QA and UAT.
• Involved in creation of Test Cases for JUnit Testing.

Environment: Java, J2EE, XML, Spring, Hibernate, Micro Services, Design Patterns, Log4j, CVS, Maven, Eclipse, Apache Tomcat, Junit, Confidential