Transformative Data Engineering and Cloud Migration Leader

Nikhil has led and executed high-impact data engineering and cloud modernization projects at industry-leading organizations like Rivian and Hinge Health. His work has consistently driven innovation in cloud migration, data pipeline design, and enterprise data management, providing a substantial impact on the business and its technological capabilities.
At Rivian, Nikhil was instrumental in modernizing the company’s data infrastructure, migrating critical data pipelines to the cloud and ensuring a seamless integration of financial and supply chain data. As part of this transformation, he designed scalable data pipelines using Apache Spark, AWS Glue, and Apache Airflow, which automated complex financial reporting processes and improved data processing speeds by 50%. Nikhil oversaw the migration of large-scale data from AWS Redshift to Snowflake, a move that enhanced reporting accuracy and made the system more efficient and accessible to stakeholders. His innovations not only streamlined reporting but also helped Rivian achieve better data governance, enabling real-time insights across the organization.
At Hinge Health, Nikhil led the architecture and development of the Unified Member Record System, a project designed to unify healthcare data and provide personalized solutions for members. This system integrated both real-time and batch data, allowing the company to develop AI-driven recommendations and health tracking systems for its members. Nikhil’s expertise in Apache PySpark, Databricks, and Apache Airflow played a pivotal role in building the data pipelines that processed massive datasets and ensured high-quality data for healthcare applications. His efforts contributed to improving patient care, member engagement, and overall operational efficiency. Nikhil's design for the Member360 Project ensured the company could offer timely health interventions, significantly improving overall member health outcomes.
The Member360 Project, which he spearheaded, unified real-time and batch data, providing Hinge Health with an innovative solution to deliver timely interventions based on predictive analytics. His leadership in utilizing Apache PySpark, Databricks, and Apache Airflow for data ingestion, transformation, and validation ensured the development of a robust and reliable system. The project improved member engagement by 30% and enhanced health outcomes for users, contributing to the company’s revenue growth.
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