Jinal Mehta
Biography
Jinal Mehta is an accomplished Senior Data Engineer based in Washington, USA, with over eight years of experience in managing complex quantitative datasets and delivering impactful business solutions. She holds a Master of Science in Data Analytics from San Jose State University and a Bachelor's degree in Computer Science from Gujarat Technological University, India. Throughout her career, Jinal has demonstrated proficiency in building scalable, secure, and efficient ETL pipelines, data architecture, data modeling, and cloud infrastructure, particularly within Amazon Web Services (AWS). Her expertise spans across Python, SQL, PySpark, Tableau, Airflow, and various statistical and data visualization tools. Currently serving as a Data Engineer II at Amazon, she is instrumental in designing ML-ready data pipelines, maintaining infrastructure, and implementing best practices in data processing. She previously held roles at HRC Fertility Management, North Orange Community College District, Herbalife Nutrition, and eClinicalWorks Pvt Ltd, where she contributed significantly to data integration, reporting automation, and strategic analytics. Her contributions earned her the prestigious Technology All Star Award at Amazon in 2025. Jinal is also an ACM Certified Reviewer and was an AWS Certified Cloud Practitioner. Beyond her technical acumen, she excels in stakeholder communication, mentoring, and operational excellence, making her a valuable asset in both technical and cross-functional teams.
Research Interest
Jinal Mehta's research interests lie in designing scalable data engineering solutions, optimizing data pipelines, and enhancing cloud-based infrastructure for efficient data processing. She is also keen on leveraging statistical analysis and machine learning-ready architectures to drive data-informed business decisions.
Abstract
From Data Chaos to Clarity: Transforming Critical Business Operations Through Data Quality Engineering
Drawing from my experience as a Data Engineer at Amazon, this session explores how poor data quality can severely impact business-critical operations and decision-making. Through a real-world case study, we'll examine how a hybrid system handling workforce data across various sites evolved from a source of operational inefficiency into a robust, automated solution.
Learn how to identify and address critical data quality challenges including duplicate entries, missing data, inconsistent uploads, and lack of validation controls that directly affect business outcomes. We'll explore practical strategies for implementing automated data quality frameworks, including real-time validation, monitoring systems, and audit trails. Discover how to build scalable solutions that not only ensure data integrity but also enable confident business decisions.
Whether you're dealing with manual processes, struggling with data reliability, or looking to enhance your data quality controls, you'll gain actionable insights on transforming data challenges into operational excellence. Join me to learn how proper data quality engineering can reduce operational risks, save countless hours of manual intervention, and support critical business functions effectively.
