• Built a production data quality monitoring service that detects and surfaces feature issues across critical ranking and ads datasets, with automated alerting and deep debugging workflows for partner teams.

• Developed a scalable Spark-based batch feature enrichment pipeline that lets ML teams assemble and experiment with thousands of features without forward-fill logic, accelerating iteration and simplifying model launches.

• Led migration of a large-scale feature store to Apache Iceberg with optimized partitioning and layout, improving query latency and reliability while significantly reducing compute and storage costs.

• Developed 5+ Ad Formats to build durable Ads business as a backend developer on large-scale ads serving and reporting services, to help Twitter capture advertiser budgets (millions) and drive better performance and brand awareness by collaborating efficiently cross-functionally with internal engineers, product managers, data scientists, and with external stakeholders and customers

• Project Lead of Deep Linking, a 3rd stack ranked project, to provide consistent and logical expected behaviors, unblocking advertisers like Amazon to spend 2M+ with new feature, and -20% cost/conversion rate

• Project Lead of App Attribution across all Campaign Objectives workstream involving 8+ teams: enabling app tracking on all objectives, allowing advertisers to measure the “halo effect” of brand campaigns and in-app conversions from deep linked web campaigns, and driving 10% incremental installs

• Demonstrated domain expertise and wrote architectural design on ads creation, storage, serving, and reporting services, performant and maintainable coding (Scala and Java), comprehensive testing to guard release readiness, and coherent data-driven analysis supporting production decisions

• Implemented critical query layers of Creative Enrichment framework to handle read-only enrichment requests from clients via GraphQL, and read/write requests from internal RESTful applications for 2 new ads products

• Mentored, influenced, and gave actionable feedback to intern, peers, and senior engineers to develop scalable and resilient products with successful task management and deliverables

• Develop and assist with new research projects focused on expanding the breadth and depth of specific advanced domain space for Amazon technology and development.

• Design, write and test new algorithms or code while partnering with a select group of experienced engineers and scientists

• Articulate technical challenges and solutions

• Handle ambiguous or undefined challenges through abstract thinking

Develop deep learning and image processing algorithms for product detection and label classification.

The System Control Center is responsible for City Light's real-time system operation and monitoring. I work closely with System Operations Engineers to provide effective operation solutions and meet all the requirements of the North American Electric Reliability Corporation (NERC) and the Western Electric Coordinating Council (WECC) reliability standards. Additionally, I develop operation horizon study models, perform specialized Power System Analysis, and provide technical supports for real-time system operators, including coordinated operation plans, pre- and post- contingency mitigation plans, and other operation guidance and solutions.