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Rupesh

Data Engineer
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Member since30+ Days ago
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Candidate Information
  • User Experience
    Experience 7 Year
  • Cost
    Hourly Rate$9
  • availability
    AvailabilityImmediate
  • work from
    Work FromOffsite
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    CategoryInformation Technology & Services
  • back in time
    Last Active OnJune 22, 2024
Key Skills
Data EngineerETL
Summary

Projects:

● Data Pipeline and ETL (December 2015 – May 2018)

● Designation – Data Engineer

● Organization - Tecblic

● Client – Technology Company, Portland, ME

o Designed and implemented a data pipeline to process semi-structured data from 100 million raw records across 14 data sources.

o Integrated data from third-party APIs to customize landing pages, resulting in a 6% improvement in paid conversion rate.

o Utilized GCP (Google Cloud Platform) services such as Google Cloud Storage and BigQuery for data processing and storage.

o Architected the data pipeline for a new product, supporting rapid scaling to 60,000 daily users.

o Led the migration from Oracle to Redshift, achieving a performance increase of 14% and saving $750,000 in 2017.

● Data Modeling and ETL Pipeline creation (June 2018 – August 2019)

● Designation – Data Engineer

● Organization - Tecblic

● Client – Health Care Company, NY o Enhanced and integrated web-based Electronic Health Records (EHR) for a Health Care Company in NY. o Collected data from SQL RDBMS, Document Files, Webpages, and APIs. o Utilized PySpark for data manipulations and processing data in parallel. o Deployed data for visualization, analytics, and front-end development using Airflow for workflow orchestration.

● ETL Process (August 2019 – Feb 2020)

● Designation – Data Engineer

● Organization - Tecblic

● Client – Payment Processing Company, CA

o The company is Irish-American financial services and software as a service company.

o Ingested streaming and transactional data across 9 diverse primary data sources using Spark, Redshift, S3, and Python.

o Created Python library to parse and reformat data from external vendors, reducing error rate in the data pipeline by 12%

o Automated ETL processes across billions of rows of data, which saved 45 hours of manual hours per month

o Built tools to provide real-time data around international currency exchange, reducing latency by 15%

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