Shril Kumar is a Senior Software Engineer with a decade of experience building large-scale backend and big data platforms, currently focused on data governance and privacy at Roku. He has designed and optimized systems that serve hundreds of millions of users—most notably real-time personalization and low-latency marketing funnels at Groupon—and delivered multi-million dollar cost savings through cloud migrations and storage re-architecture. At Roku he architected a GDPR Data Deletion Framework using cryptographic erasure and built a high-throughput PII scanning engine that blends regexes with a Qwen 2.5 model to cut licensing costs and vastly improve request throughput. Proficient in Java, Python, Scala and distributed systems like Spark, Kafka, Hive and Airflow, he also contributes to open-source data tooling—adding DataFrame features to Koalas to improve pandas-on-Spark usability. Based in Bengaluru and trained at IIT (ISM) Dhanbad, he combines hands-on engineering with strategic ownership, mentorship, and a habit of tackling problems he initially “doesn’t know how to” solve.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Technology (Hons.), Engineering, Bachelor of Technology (Hons.), Engineering at Indian Institute of Technology (Indian School of Mines), Dhanbad
ISC (12th Standard), High School/Secondary Certificate Programs, ISC (12th Standard), High School/Secondary Certificate Programs at The Chintels School
English, Hindi, Russian
Stackoverflow
Stats
152reputation
13kreached
14answers
6questions
Github Skills (17)
spark10
python10
data-science10
dataframes10
pandas10
big-data10
dataframe10
testing9
documentation9
ruby-on-rails6
google-cloud-vision6
pdf6
ocr6
pyspark6
amazon-web-services6
Programming languages (9)
TypeScriptJavaCSSRustScalaJavaScriptRubyRich Text Format
Contributions:2 reviews, 7 commits, 12 PRs in 2 months
Contributions summary:Shril primarily contributed to the documentation and implementation of Koalas, a pandas API on Apache Spark. Their commits include adding inline documentation for various DataFrame methods such as `isnull`, `notnull`, `iteritems`, `dropna`, `drop`, and `get`. Further contributions focused on adding and testing the `to_excel` and `to_records` methods for DataFrame conversion, as well as the `cache` method. These changes enhance the usability and functionality of the library for data manipulation and analysis within a Spark environment.
Contributions:16 pushes, 2 branches in 6 years 2 months
ruby-on-railsrailshartlrubymichael
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