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Pramath Parashar

@pramathparashar

Data Engineer at BHP Minerals Service Company who automated data quality testing and cut data validation errors by 60%.

At BHP Minerals Service Company, I apply data privacy techniques, including PII masking and anonymization, across daily business records. I also enforce access controls and Snowflake column-level security.

I automated data quality testing with Great Expectations, cutting data validation errors by 60%. My business data analysis and document reviews also improved data accuracy verification by 30%.

Earlier, as a Student Employee - Data Engineer at Kent State University, I implemented schema validation and automated testing pipelines. At Pioneer Instruments, I conducted data quality assurance and developed SQL queries to analyze business records.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

BHP Minerals Service Company

Sep 2024 - Present (2 years)

Applied data privacy techniques including PII masking and anonymization across 4,000 daily business records, ensuring full compliance with data privacy regulations. Implemented rigorous data quality assurance processes, identifying and resolving inconsistencies and anomalies to improve accuracy and completeness by 45%.

Student Employee - Data Engineer

Kent State University

Feb 2024 - May 2024 (3 months)

Developed structured logging and defensive programming that identified and resolved over 150 data quality anomalies, enhancing dataset accuracy and integrity. Implemented Pydantic schema validation and JSON serialization to enforce data privacy and PII protocols, improving compliance documentation and stakeholder understanding.

Data Quality Assurance Data Validation Data Cleaning Data Standardization Data Normalization Schema Validation Data Privacy PII Pydantic JSON PostgreSQL SQL Docker GitHub Actions Pytest Python

Data Operations Analyst

Pioneer Instruments

May 2019 - Aug 2022 (3 years 3 months)

Conducted comprehensive data quality assurance through cleaning and validation, resolving over 1,200 anomalies to improve business data reliability. Developed advanced SQL queries using window functions to analyze 10,000+ business records weekly, delivering insights to improve milestone tracking and project performance.