Kashif Faraz
Staff Software Engineer at Imply
Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolKashif Faraz is a Staff Software Engineer based in Karnataka, India, with over a decade of experience building high-performance data platforms and databases. He currently contributes to Apache Druid at Imply as a PMC member, focusing on real-time ingestion, compaction, and cluster scalability, and has implemented features like /lockedIntervals and robust compaction tests. Prior roles span AppDynamics, Qubole, and several data-platform-focused companies where he developed metric pipelines, SQL workflow managers, and Hive-based systems. He blends deep backend engineering with production-grade testing and configuration work, often tackling edge cases in distributed query handling. Notably, his open-source contributions improve Druid’s resilience and operational controls—a sign of practical, systems-level expertise. Trained at IIT Kharagpur, he pairs strong fundamentals with hands-on experience shipping scalable data infrastructure.
10 years of coding experience
7 years of employment as a software developer
Bachelor of Technology (BTech), Electrical, Electronics and Communications Engineering, Bachelor of Technology (BTech), Electrical, Electronics and Communications Engineering at Indian Institute of Technology, Kharagpur
English, Urdu, Hindi, Bengali
Github Skills (13)
data-storage10
javas10
databases10
api10
apache-druid10
back-end-development10
relational-databases10
apidoc10
sql-database10
java10
database10
testing10
performance-optimization9
Programming languages (4)
Github contributions (5)
apache/druid
Jun 2021 - Jan 2023
Apache Druid: a high performance real-time analytics database.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 2517 reviews, 105 commits in 1 year 6 months
Contributions summary:Kashif primarily focused on enhancing the Apache Druid database by implementing new features and fixing bugs related to data compaction and query handling. They added new APIs, such as `/lockedIntervals` to skip compaction for locked intervals, and implemented various tests, like `ITAutoCompactionLockContentionTest`, to verify the behavior of the system in edge cases. Additionally, the user addressed issues with handling SQL queries by the router, and added a new configuration option, `druid.broker.segment.ignoredTiers`. Their contributions centered around ensuring the robustness and efficiency of Druid's core functionalities.
real-timebig-datadruiddatabasehadoop
kfaraz/druid
Apr 2021 - Apr 2025
Apache Druid: a high performance real-time analytics database.
Contributions:45 PRs, 1532 pushes, 588 branches in 4 years
real-time-analyticsanalyticssqlapachebig-data
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.