Position Hadoop Developer Resume

SUMMARY:

6 years of total IT experience in Big Data & JAVA technologies and Testing Technologies.
3 years of experience in Hadoop Distributed File System(HDFS), Map Reduce framework, Yarn, Hive, Kafka, Storm, HBase, Oozie, Java Mapreduce, Spark.
Production experience on Hadoop Distributed File System(HDFS), Hadoop Mapreduce framework, Hive, Hue, Oozie, HBase, Spark and Cloudera Manager.
Experience with Hadoop Big Data installation and development.
Expertise in Object Oriented Analysis & Design and Java development.
Load and transform large sets of structured, semi structured and unstructured data.
Adequate knowledge and working experience in agile methodologies.

TECHNICAL SKILLS:

Programming Languages: C, C++,Java, Pig Latin, Scala,R

Big Data Technologies: Hadoop,MapReduce,Pig,Hive,YARN,Oozie,Kafka,Storm,Cloudera Manager,Spark

Scripting Languages: SQL,HQL vBScript

Database: HBase,Neo4j,MongoDb,Oracle

Tools: Oracle 9i, Informatica Power Center 8.6, HP Quality Center 10.0, VMWare,UFT,QTP,RStudio

PROFESSIONAL EXPERIENCE:

Confidential

Hadoop Developer

Responsibilities:

Worked as active developer for implementation of compliance data store analytic platform in Hadoop using hive and HBase .
Involved in migrating EBCDIC format data to Hadoop and conversion to ASCII format.
Developed multiple MapReduce jobs in Java for data cleaning and preprocessing
Involved in managing and reviewing Hadoop log files and uploading the final results and written queries to analyze them.
Develop, validate and maintain HiveQL queries.
Running reports in Hive Queries.
Analyzing data with Hive
Designed Hive tables to load data to and from external files.
Supported code/design analysis, strategy development and project planning.
Requirement analysis, design, development, testing and production rollout.

Confidential

Automation Test Engineer

Responsibilities:

Analyzing the requirements and creating the test strategy documents and designing test cases
Maintaining the automation framework using QTP.
Manual execution of the test cases both regression and functional.
Creating, reviewing, validating and maintaining a comprehensive automated regression test suite.
Identifying Automation Scenarios from the set of Test cases identified in the sprint
Develop Automation Test cases for the identified Automation Scenarios
Run Smoke regression suite after every build and publish the detailed reports with the bugs detected
Run Full Automation suite for every Release and analyze the root cause
Showcasing the automated test cases to the Product owner before Sprint signoff
Participating in Scrum meetings and Retrospective meetings to analyze the Sprint Health
Reviewing the Code prepared by other team mates and suggesting improvements if required.
Working closely with development/business teams for reproduce, analyze and debug issues and requirement clarifications.
Maintaining defect log.
Working in BIG Data - Analytics, JAVA Programming, and Object Oriented Design & Concepts.
Working with the Hadoop stack (MapReduce, HDFS, Pig, Hive, HBase, Oozie and Zookeeper).
Configuring and administering the Hadoop Cluster using major Hadoop Distributions like Cloudera.
Working on Spark frameworks with Scala.
Working with analytics on NoSQL databases like HBase,MongoDb,Neo4j.
Applying Machine Learning, Deep Learning and Data mining concepts with Python programming.
Testing MapReduce programs using MRUnit and Junit
Using RDBMS concepts and worked with Oracle, SQL Server and My SQL.
Working with SAS Enterprise miner and Tableau for Data Analytics.
Working on data analysis projects using R Studio.
Built a working model of an IOT Smart smoke detector.

Confidential

Responsibilities:

Applied machine learning concepts to detect if a credit card customer will be at default based on some predictors like Credit Limit,Repayment Status,Repayment Amout,Previously defaulted by how many months.The target was a categorical variable default which was set to 0 if a customer was not a defaulter and 1 if he was a defaulter.
Machine Learning Algorithms Applied: Logistic Regression,Support Vector Machine,K Nearest Neighbours,Neural Networks
Tools Used:Matlab,Pycharm for Python,Tableau for data visualization,H2o Package

Wine Magazine Reviews Analysis

Confidential

Responsibilities:

Applied Data mining concepts to a Wine Reviews Magazine Dataset.
Model 1:Target was variety at Red/White Wine and the predictors were Points and Price. Used cluster dendogram to hierarchichally cluster Redness and Whiteness words from the reviews description.. Developed Redness and Whiteness scores based on top frequent words. With this we built a decision tree to predict the Variety at White/Red wine
Model 2: Classify a wine based on the points given to it.A wine was scored anywhere from 80 to 99 points .The predictors were country,price of the wine,Variety and Winery.Target was points falling in two categories, wine above 90 points and wines below 90 points.
Algorithms Applied:CART,Random Forest Classification,Bagging and Boosting to CART
Tools Used:Python

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