Aug 2025 – Present · 9 mos

• Working on designing and optimizing a cloud-agnostic backup platform handling multi-billion object datasets across object storage systems such as Amazon S3 and Azure Blob Storage. Driving architecture decisions around scalability, reliability, and security, ensuring backup systems remain robust even under extreme data growth and failure scenarios.
• Focused on:
• Designing incremental backup strategies with efficient change detection at scale.
• Leveraging native capabilities like change feeds and inventory mechanisms in Azure to identify deltas with minimal overhead

Oct 2019 – Aug 2025 · 5 yrs 10 mos

• Led Commvault’s Indexing team, a globally distributed group of 12 engineers responsible for cataloging and managing petabyte-scale data within Commvault’s data protection platform.
• Owned end-to-end delivery of the indexing subsystem, including roadmap planning, execution, and operational excellence. Partnered with product management and cross-functional teams to translate business requirements into scalable, high-performance solutions, ensuring timely and high-quality feature releases.
• Built and managed a high-performing engineering team by mentoring developers, setting clear goals, and fostering a culture of ownership, collaboration, and continuous improvement. Played a key role in hiring, onboarding, and developing talent across geographies.
• Led initiatives to improve system reliability, scalability, and performance at scale, while proactively managing critical customer escalations and driving root-cause analysis and resolution.
• Drove the design and delivery of a analytics platform built on index metadata, applying time series analysis of data growth patterns to unlock multiple capabilities, including anomaly detection (e.g., ransomware signals), predictive storage growth alerting, user quota management, and proactive infrastructure capacity planning.

Oct 2010 – Oct 2019 · 9 yrs

• Led the re-architecture of a next-generation indexing framework to power large-scale search and analytics, enabling ingestion and indexing of hundreds of millions of records per day with strict performance and reliability requirements.
• Defined the architectural vision and drove key design decisions across data modeling, distributed ingestion pipelines, indexing strategies, and query execution. Built a horizontally scalable, fault-tolerant system leveraging partitioning, parallel processing, and efficient storage layouts to support sustained high-throughput workloads.
• Provided technical leadership across teams, guiding implementation, enforcing design rigor, and establishing best practices for scalability, observability, and resilience. Influenced cross-team architecture to ensure seamless integration with upstream data pipelines and downstream query services.
• Led deep performance engineering efforts, identifying and eliminating bottlenecks across ingestion and query paths, resulting in predictable low-latency query performance at scale. Designed and executed large-scale benchmarking and stress testing frameworks to validate system behavior under production-level workloads.
• Led a major optimization of the inter-service messaging layer, achieving ~300× improvement in throughput and latency. Identified inefficiencies in reflection-heavy metadata handling and redesigned the internal metadata representation for generated classes, significantly reducing runtime overhead. Re-architected the wire protocol by transitioning from XML-based serialization to a compact, platform-agnostic binary format, resulting in substantial gains in serialization/deserialization performance, reduced payload size, and improved network efficiency.