Yashesh Pandya Immediate joiner || Data Engineer || Data Scientist || Data

Research Assistant

University at Buffalo

Jun 2023 - Aug 2023 (3 months)

• Research and collect data. • Interprets, synthesizes, and analyses data. • Development of research project prototype. • Report on the status of research activities over regular meetings. • Write and Submit research paper in conference/workshop

Data Engineer

Freddie Mac

Jun 2022 - Current (4 years)

• Led critical project at Freddie Mac to optimize data processing and pipelines for mortgage-backed securities, enhancing decision-making speed and accuracy. • Developed high-performance data pipeline handling 1.5TB daily data ingestion from diverse sources, enabling efficient data processing. • Leveraged cutting-edge tools: Apache Spark for distributed and parallel data processing, achieving 30% reduction in processing time. • Utilized Apache Airflow to orchestrate and monitor data workflows, ensuring timely execution and improved efficiency. • Employed Amazon S3 and Redshift for scalable data storage and retrieval, supporting the system's ability to handle increasing data volume. • Collaborated closely with data analysts and stakeholders

Student Assistant

University at Buffalo

Jan 2022 - Jun 2022 (6 months)

• Scanned and cataloged library equipment, ensuring accurate records of inventory. • Conducted equipment tests during check-in and check-out to verify functionality, enhancing user satisfaction. • Maintained an organized and easily accessible database to track equipment loans and returns, improving overall efficiency. • Assisted patrons with inquiries, providing exceptional customer service and fostering a positive library experience. • Managed paperwork and forms related to equipment loans and returns, maintaining accurate records.

E

Data Engineer

Exert Infotech

Jun 2019 - Dec 2021 (2 years 7 months)

• Led challenging project at Exert Infotech to optimize data processing and analysis for financial services client. • Improved data accuracy, reduce processing time, and enhance data reliability for better decision-making. • Leveraged Apache Spark for distributed data processing, achieving 40% reduction in processing time. • Implemented data validation and quality checks using Python and SQL, resulting in 98% data accuracy. • Utilized cloud-based solutions like Amazon S3 and AWS Glue for seamless scalability and efficient data storage and ETL processes. • Collaborated closely with multidisciplinary team, data analysts, and business stakeholders. • Ensured data security and privacy throughout the project and showcased expertise in data engin

Yashesh Pandya

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