Khalil Greenidge
Software Engineer @Sony Music Entertainment
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WORK HISTORY
Software Engineer @Sony Music Entertainment
London, GB
Owned and delivered end-to-end features using Python/FastAPI backend and React/TypeScript frontend, connected via Apollo GraphQL, enabling real-time financial insights for 100k+ artists and labels.• Architected and implemented Redis caching layer from concept to production, cutting API response times by 99%(2.14s → 5ms) across high-volume financial endpoints.• Discovered critical authentication vulnerability, architected and led end-to-end remediation and implemented OAuth 2.0/JWT migration with Auth0 and Neo4j RBAC, eliminating unauthorized access risk across the team\'s microservices.• Drove modernization of backend infrastructure by leading Python & FastAPI upgrades, redesigning AWS Lambdas, and improving CI/CD pipelines for greater reliability and speed.• Reduced unit test runtime by 94%(17 min 1 min) using pytest with in-memory database transformation cutting deployment time by half; maintained Jest and React Testing Library coverage for frontend components.• Championed data and observability improvements, optimizing Snowflake SQL and dbt models (95% faster queries) and deploying Datadog/Sentry dashboards that reduced incident resolution time by 40%.• Provided technical mentorship by sharing performance-optimization strategies and best practices that improved team efficiency and code quality.
EDUCATION
University of Birmingham
Master of Science - MS, Computer Science
Samuel Jackman Institute of Technology
Diploma in Microcomputer Technology, Electronics
University of Nottingham
Bachelor's degree, Business Management
Barbados Community College
Associate of Arts and Sciences - AAS, Compter Studies
ABOUT KHALIL GREENIDGE
Fullstack software engineer and A.I enthusiast with interests in technologies such as module bundling (Web pack), Python, JavaScript (including React and NodeJS), PHP, Java/SpringBoot, Kotlin, MongoDB/NoSQL, and SQL. Collaborated in an agile software development environment with multi-national teams across the globe from Canada, USA, UK and Germany to improve the shipping experience of over users.Developed across a large scale distributed system infrastructure and stack, with over applications, including internal and external APIs, enterprise systems and tools.Conducted research in Information Retrieval, and Artificial Intelligence such as Machine Learning and Natural Language Processing, with Unsupervised and Supervised ML Algorithms like K-Nearest Neighbours and Naïve Bayes to improve résumé selection with a precision of 95%.
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