Umang Sharaf
Lead Data Engineer
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolUmang Sharaf is a Lead Data Engineer with 11 years of experience building and scaling data platforms for high-throughput analytics at Chartmetric, where his teams power real-time insights across millions of artists, tracks, and playlists. He specializes in orchestrating reliable pipelines and cost-efficient infra using Airflow, ClickHouse, Snowflake, Postgres and Elasticsearch, and has driven measurable cloud cost reductions through query and infrastructure optimizations. At Chartmetric he has owned end-to-end data delivery to major label customers via AWS S3, Snowflake, Databricks and GCS while growing and managing a 10+ person Data organization. His background blends rigorous ML training from UC Berkeley (including RL work on traffic control) with hands-on engineering—from ingestion and ES indexing to production ML ranking—so he bridges research and production needs. An obsessive personal data collector and former iOS indie publisher, he brings a detail-oriented, product-minded approach to data engineering and pipeline design.
11 years of coding experience
8 years of employment as a software developer
Master of Engineering - MEng Computer Science, Master of Engineering - MEng Computer Science at University of California, BerkeleyBachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at The Ohio State University
English, Gujarati, Hindi, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
traffic-simulation10
sumo10
python10
xml-parsing10
reinforcement-learning9
file-handling8
file-processing8
fileio8
github8
file-access8
autonomy7
benchmark6
benchmarking6
Programming languages (5)
Github contributions (5)
flow-project/flow
Oct 2018 - Dec 2019
Computational framework for reinforcement learning in traffic control
Role in this project:
Back-end Developer
Back-end DeveloperContributions:117 commits, 9 PRs, 103 pushes in 1 year 1 month
Contributions summary:Umang primarily contributed to the `flow-project/flow` repository, which focuses on reinforcement learning for traffic control. Their work involved modifications to `NetFileGenerator` in the context of a specific scenario involving XML configuration files, likely used for defining the network topology and vehicle behavior within the SUMO traffic simulator. These changes included updating file paths, importing and processing vehicle and route data from external XML files, suggesting a focus on data integration for traffic simulation environments. Additionally, the user incorporated changes that would merge branches, signifying work on ongoing development efforts.
reinforcement-learningbenchmarkautonomousvehicle-controlsumo
umangs94/umangs94.github.io
Nov 2017 - Jan 2020
Contributions:89 pushes, 1 branch in 2 years 2 months
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