Malhar Parikh
Hi, I’m Malhar Parikh, a software engineer focused on building scalable, cloud-native distributed systems using Java, Python, and AWS/GCP.
About
Hi, I’m Malhar Parikh, a software engineer focused on building scalable, cloud-native distributed systems using Java, Python, and AWS/GCP. I enjoy delivering 0-to-1 features across security, analytics, and inventory domains, operating in Agile environments with CI/CD-driven SDLC.
I thrive on solving complex performance and reliability challenges, from reducing latency to automating data pipelines and real-time analytics. Outside work, I mentor students, contribute to data pipelines, and explore new tech in cloud, security, and backend engineering.
- 1 year's experience
- Joined March 2026
Skills· 19
Languages
- EnglishFluent
Industry experience
- Software & Internet
- Education
- Computers & Electronics
- Professional Services
Work experience· 5 roles
Teaching Assistant for database systems courses; supported students in SQL/NoSQL, distributed systems, Scala, Spark and Kafka, and assisted with data engineering principles and code reviews.
Engineered replay attack prevention for a SaaS passwordless authentication platform serving enterprise clients, using Java Spring Boot, Strategy pattern, and Redis TTL-based nonce expiry, securing over 1,500 authentications per second with zero reported breaches; built a real-time analytics microservice in Java Spring Boot to process FIDO authentication logs and compute KPIs, exporting metrics to Splunk for automated dashboards, accelerating failure diagnosis by 50%; developed REST APIs for real-time “Impossible Travel” detection by identifying geolocation outliers; implemented a push notification service for UAF authentication enabling real-time out-of-band approval flows; reduced auth latency by 30% with an AWS EventBridge–Lambda pipeline automating transient data cleanup in PostgreSQL.
Mentored 200+ students in SQL/NoSQL databases & distributed systems, covering query optimization & data engineering principles; directed code reviews for complex data pipelines, enforcing best practices in Scala, Apache Spark and Kafka-based event architectures.
Education
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