San Francisco Bay Area • Staff Software Engineer
Distributed Systems • Telemetry • Real‑Time Data • Geospatial
Website: https://kuanbutts.com
Email: [email protected]
LinkedIn: https://www.linkedin.com/in/kuanbutts
GitHub: https://github.com/kuanb
Staff-level backend engineer focused on large-scale distributed systems, real-time telemetry, and high-throughput data infrastructure. 10+ years building low-latency ingestion pipelines, scalable event-driven architectures, and production ML inference systems handling billions of messages/hour. Strong systems thinker with product intuition, especially where geospatial, mobility, and time-series data meet.
San Francisco, CA | 2019–Present Lead engineer for global real-time traffic and telemetry systems.
Selected accomplishments:
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Architected and shipped a distributed inference pipeline processing 5B+ location observations/hour for real-time global speed estimation.
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Rebuilt the speed-calculation system into a decoupled, resilient, ML-driven architecture, improving:
- Surface-street coverage: +60%
- Speed-estimation accuracy (F1): +11%
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Designed telemetry + sensor‑fusion models for modality classification, anomaly detection, and segment-level behavioral inference.
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Delivered a global daily activity index with block-level analytics powering enterprise mobility and freight intelligence products.
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Led major reliability and cost-optimization initiatives across ingestion, stateful matching, distributed inference, and serving.
Tech: Python, TypeScript, AWS/GCP, Kafka/Kinesis, Spark, Airflow, Redis/ValKey, Docker, Cloudformation/CDK
Berkeley, CA | 2017–2019 Backend/data engineer for a geospatial analytics platform.
- Built high-performance spatial graph algorithms and custom routing engines.
- Developed large-scale ETL systems for mobility, land‑use, parcel, and telemetry datasets.
- Improved runtime and reliability of multi-hour geoprocessing pipelines.
Tech: Python, TypeScript, Postgres/PostGIS
San Francisco, CA | 2016
- Scaled ClientComm from single-tenant to multi-tenant SaaS adopted by multiple jurisdictions.
- Implemented ingestion, analytics for high-volume SMS communications.
Tech: Python, Flask, Node.js, Postgres, Redis, Heroku/Azure
New York, NY | 2015
- Built real-time ingestion pipelines for NYC MTA vehicle telemetry.
- Prototyped real-time traffic signal monitoring and mobility analytics.
- Worked with Azure, event-streaming infra, and cloud-scale data processing.
Cambridge, MA | 2012–2015
- Developed mobile + web data-collection platforms and large-scale geospatial data-processing pipelines.
- Built interactive analytics tooling for interdisciplinary transportation research.
Master’s (MCP), Transportation Systems | 2012–2014
Technical focus: computational methods for mobility and large-scale data processing.
Thesis: Iterative Methods for Large-Scale Mobile Data Acquisition
Bachelor of Science | 2007–2011
Coursework included computational design and algorithmic modeling (Grasshopper).
Languages: Python, TypeScript/JavaScript, SQL
Infrastructure: AWS, GCP, ECS/Kubernetes, Docker, Cloudformation/CDK
Data / Streaming: Kinesis/Kafka, Spark, Airflow, Parquet
ML / Analytics: scikit-learn, LightGBM, feature engineering
Datastores: Postgres, PostGIS, Athena, BigQuery, DynamoDB, Redis/Valkey
Domains: distributed systems, telemetry pipelines, real-time analytics, event-driven architectures, ETL/ELT, observability