Haranesh Sokharaya

HARANESH SOKHARAYA Data Analyst / Data Engineer CONTACT ME PHONE - EMAIL- SKYPE haranesh.sokharaya PROFILE ME A driven and analytical professional with a passion for data and 15+ year of experience in the field. Skilled in data visualization, data analysis, and statistical modelling, I have a proven track record of delivering actionable insights and recommendations to improve business operations. Experienced in using data tools such as SQL, Python, and Power BI to extract, process, and manipulate large datasets. Committed to continuous learning and staying up-to-date with industry trends to provide value to organizations EXPERIENCE Data Analyst / Data Engineer - Full time Freelancer [Top Rated Plus on Upwork] 2017 / Present Sr Software Developer - IBM India 2014 - 2017 Jr Software Developer – Other 2011 - 2014 SKILLS - Power BI - DAX - Azure Pipeline - SQL - SSMS / SSIS / SSAS - Microsoft Dataflow - Azure Logic Apps - Azure Functions - Azure Batch Services - Share Point - Excel / Power Pivot - Power Query / MDX - Business Intelligence - IIS Server - Apache / Tomcat Server - Azure Databricks - Data Ware Housing - Azure Data Lake - AWS Textract - AWS Quick Sight - Docker - ETL & ELT - My SQL - AWS Time Stream - PostgreSQL - MongoDB - Power BI Web App - Oracle - Google Big Query - Data Cleaning - API Integration - SSL setup - Pandas & NumPy - Azure Delta Lake - Hadoop / Big Data / Spark - OpenCV - BOW – ML Model - Python - AirByte & Airflow - Azure Synapse - JSON - Restful APIs - SOAP - XML - Limelight - CRM - Google Sheet - Java - Linux and administration - Data Scraping - Selenium Automation - QuickBooks integration - Beautiful soup & Scrapy - JDBC - Node JS - Flask - Sentiment Analysis - ML United States - 7 United Kingdom - 2 Canada - 2 India - 5 Israel - 1 South Korea - 1 China - 1 Singapore - 1 CLIENT COUNTRY HARANESH SOKHARAYA Data Analyst / Data Engineer PROJECTS PROJECT - 1 ETL & Power BI Dashboard for US call centre [-] Role: Power BI Developer & Data Engineer Project Goal: End to end reporting solution using various data sources. Solution: - Setup centralize reporting using MS-SQL database on azure premise. - Ingesting various data sources to MS-SQL using azure pipelines. (Data sources like Google Sheet, MySQL, Limelight / Sticky CRM, Five9 calls details) - Using SSAS-Tabular modelling created various reports Result: - Solved business challenges - The set of 10+ reports offer comprehensive insights on various aspects such as dropped calls, call duration, abandoned calls, execution times, and backend information related to call quality. These reports provide valuable information that can help businesses make informed decisions with precision PROJECT - 2 Power BI Dashboard for Online Sales – Order Processing Team [-] Role: Power BI Developer & Data Engineer Project Goal: End to end reporting solution using various data sources. Solution: - Setup centralize reporting using MS-SQL database on azure premise. - Ingesting various data sources to MS-SQL using azure pipelines. (Data sources like Google Sheet, MySQL, Limelight / Sticky CRM) - Using SSAS-Tabular modelling created various reports Result: - Solved business challenges - The set of 5+ reports offer comprehensive insights on various aspects such as dropped calls, call duration, abandoned calls, execution times, and backend information related to call quality. These reports provide valuable information that can help businesses make informed decisions with precision PROJECT - 3 Web Scraping for real time crypto transaction records [-] Role: Utilized python programming skills in professional capacity HARANESH SOKHARAYA Data Analyst / Data Engineer Project Goal: Retrieve data for tracking cryptocurrency transactions Solution: - Set up email alerts for monitoring cryptocurrency transactions in real-time. Result: - Resolved the project's intended purpose PROJECT - 4 ETL – Schedule daily refresh data from Google Sheets to Database [-] Role: Utilized Airbyte tool expertise to achieve goal Project Goal: The project's primary objective was to retrieve data from Google Sheets and insert it into Google Big Query. Solution: - To achieve the project goal, I utilized my expertise in Docker and Linux server setup to set up the environment. Then, I configured the sources and scheduled daily data refresh. Result: - The project was completed successfully, and the daily data refresh was achieved as planned, meeting the project's objectives. PROJECT - 5 ML – Document Classification for Imagin centre [-] Role: AI / ML Engineer Project Goal: The primary objective of the project is to develop a machine learning model that can classify medical documents into different categories. The model will be trained on a labelled dataset of medical documents, and will use various machine learning algorithms and techniques to learn the patterns and features that distinguish the different categories of documents. The model will be evaluated on a test dataset to measure its performance and accuracy in classifying new, unseen documents. Solution: - The categories that the model will classify the documents into may include things like medical specialties, medical conditions, treatments, or procedures. The model's accuracy in classifying the documents into their correct categories will be crucial for its usefulness in real-world applications, such as helping doctors and researchers quickly search for relevant medical documents, or automatically categorizing and organizing large collections of medical documents. HARANESH SOKHARAYA Data Analyst / Data Engineer Result: - The final product was delivered to the client, who was satisfied with the performance and functionality of the machine learning model. The client could use the model to quickly search for relevant medical documents or automatically categorize and organize large collections of medical documents. The success of the project demonstrates the usefulness of machine learning in solving real-world problems and highlights the importance of accurately labelled datasets for training and evaluating machine learning models. PROJECT - 6 Automate daily refresh of multiple data sources into a central Database. [-] Role: ETL Data Engineer Project Goal: The project's primary objective was to retrieve data from various sources and insert it into Microsoft SQL Server. Solution: - To achieve the project goal, I utilized my expertise in Airflow and Python. Then, I configured the sources and scheduled daily data refresh. Result: - The project was completed successfully, meeting the client's requirements to retrieve data from various sources and insert it into a MSSQL database. The data refresh process was automated to run daily, ensuring that the database was updated with the latest data from the sources. PROJECT - 7 Develop an Azure Pipeline for processing file. [-] Role: ETL Data Engineer Project Goal: The project's objective was to automate the process of cleaning data in files received via FTP, update the cleaned data in Salesforce, and send mail notifications upon completion. The project aimed to improve data quality and reduce manual effort in processing the files. Solution: - To achieve this, an automated pipeline was developed that periodically retrieved files via FTP, cleaned the data using appropriate tools and techniques, and then updated the cleaned data in Salesforce. The pipeline was configured with appropriate error handling mechanisms and logging to ensure data integrity and reliability. Result: - Overall, the success of the project demonstrates the importance of automating data processing pipelines to improve data quality and reduce manual effort in data processing tasks. The client was satisfied with the performance and functionality of the system, which enabled them to make better-informed decisions based on the latest, high-quality data. HARANESH SOKHARAYA Data Analyst / Data Engineer