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Data Engineer

Salt Lake City, UT
January 31, 2019

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Rajit Nikhare

Data Engineer

Personal Information


**** **** *** *****

Salt Lake City, Utah - 84102




Date of birth




Technical Skills

Languages and Database: Python, SQL, R,

Java, MS-SQL, PostgreSQL


Statistics & ML : Supervised and

unsupervised learning, hypothesis testing,

cross-validation, A/B testing, clustering, NLP,



Advanced knowledge of MS Excel : macros,

pivot tables, data visualization


Big data ecosystem: Hadoop, HDFS,

MapReduce, Pig, Hive


Tools and IDE: R Studio, Jupyter, Git


Data Visualization Tools : Tableau, ggplot,



Cloud/Infrastructure: Microsoft Azure, AWS,

MS Server 2012 R2, Linux, Docker, AWS

Elastic load Balancers, Lambda, SaaS,

CloudFront, DNS, SSL


Certification: AWS Solutions Architect



Software professional with three years of industry experience. I have strong technical knowledge in Python, SQL, AWS,Tableau and an ability to find solutions under critical scenarios. As an individual, I seek more knowledge in the field of Data Science and Engineering. I am an AWS Certified Solutions Architect as well. Open for relocation. Experience

Feb 2018 -

Jun 2018

IT Analytics Intern

Carbonite Inc.

• Designed a regression model to predict memory efficiency on AWS and Azure systems using R

• Analyzed data using Kibana, Elasticsearch and Logstash to improve resource utilization

• Created Tableau dashboards for performance monitoring using data from SolarWinds

• Analyzed infrastructure data to find KPIs and other relevant metrics for business growth Jan 2018 -

Jul 2018

Capstone Project Intern


• Built Linear Regression model for feature selection on the basis of the p-values and shortlisted 30 significant feature variables from a large data-set of 1 million rows and 300 variables

• Built RandomForest model for increasing the accuracy and the R2 value of the prediction model

• Built a scalable AWS Data Pipeline to enable secure transfer of data from Berkadia environment to AWS services like S3, EC2 and Redshift

• Developed an R Shiny application for predicting rent of the housing properties in USA. Accuracy on test data: 85.55%

Jan 2018 -

Feb 2018

Research Assistant

University of Utah, David Eccles School of Business

• Parsed text from crypto-currency websites using BeautifulSoup 3 to understand the market-price variations

• Analyzed text using Gunning-fog index and Flesch Reading Ease counts for 1000+ pdfs using Natural Language Processing on Python with NLTK library Feb 2015 -

Jun 2017

System Administrator

Larsen & Toubro Infotech

• Managed 500 VMs in the production environment for United Technology Corporation on MS Azure platform

• Configured Express Route and load balancing for production web servers on MS Azure

• Designed scalable and reliable IaaS solutions for on-premise servers to Azure cloud connectivity

• Created Python and PowerShell scripts to automate tasks on MS Azure environment using runbooks

• Developed and managed ETL Data pipelines on MS Azure

• Used SQL queries for database backup performance tuning on MS Azure

• Collaborated with UTC clients from different countries to troubleshoot production issues on the Kony application


Aug 2017 -

Aug 2018

University of Utah, David Eccles School of Business Master of Science in Information Systems, GPA: 3.8

• Course Work: Marketing Analytics, Database Theory & Design, Data Mining, Statistics & Predictive Analytics, System Analysis & Design, Machine Learning with Python, Data Structures, Data Visualization, ETL

Jun 2010 -

Jun 2014

Rajiv Gandhi Prodyogiki Vishwavidyalaya, College of Engineering Bachelor of Engineering in Electronics and Communication, GPA: 3.4 Academic Projects

Image Recognition

• Analyzed MNIST data-set for handwritten digit pattern recognition

• Designed a prediction model for handwritten digits using CNNs with 99.9% accuracy (TensorFlow) Twitter Analytics

• Collected tweets using Twitter API to analyze tweets about Super-Bowl advertisements

• Analyzed sentiments of the tweets about Super-Bowl 2018 advertisements (Python,JSON) Kaggle Competition

• Cleaned, transformed and analyzed the data-set of Ames, Iowa household

• Predicted house prices for Ames, Iowa using linear regression techniques. Accuracy 87% (R)

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