UMANG GOEL
Software Engineer with strong foundations in data structures, algorithms, concurrency, and systems programming. Proficient in C++, Java — experienced optimizing performance-critical components through profiling, memory analysis, and concurrency control. Passionate about building high-performance, scalable storage and data systems.
TECHNICAL SKILLS
Arizona State University · B.S. Computer Science · Graduating May 2026
I'm a Computer Science student at Arizona State University with strong foundations in data structures, algorithms, concurrency, and systems programming. Experienced optimizing performance-critical components through profiling, memory analysis, and concurrency control.
Demonstrated ability to improve latency and memory efficiency in distributed systems. Graduating May 2026 and passionate about building high-performance, scalable storage and data systems.
Arizona State University
2022 – 2026
B.S. Computer Science
Coursework
Core Concepts
EXPERIENCE
Honeywell & ASU Innovation Hub
Software Developer Extern
Tempe, AZ
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Developed performance-sensitive backend components in C++ and C#, reducing end-to-end latency by 25% through architectural improvements and efficient data handling.
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Designed concurrent ETL pipelines with Airflow to ingest and transform 50K+ records, managing PostgreSQL schemas and Redis NoSQL databases for effective database management and enabling reliable, repeatable processing for analytics and model training environments.
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Profiled compute-intensive modules using memory and CPU profiling tools, reducing memory usage by 40% and improving runtime efficiency under high-load conditions.
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Applied concurrency control and thread-safe design patterns to ensure correctness and stability in multi-threaded environments.
DMML Lab, Arizona State University
Software Developer Intern
Tempe, AZ
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Designed and implemented distributed backend services in C++ and Java, applying microservice and gRPC-based communication patterns to reduce p95 latency by 30% across real-time workloads.
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Built high-throughput data pipelines processing 300K+ records, applying concurrency control, retry mechanisms, and memory-efficient data handling to improve analytical accuracy by 15%.
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Developed event-driven systems using RabbitMQ with asynchronous consumers, sustaining 500+ events/min with fault tolerance and resilient load distribution.
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Improved system reliability and maintainability by introducing unit testing (TDD), automated CI/CD pipelines, and performance monitoring, reducing production defects and accelerating release cycles.
PROJECTS
High-performance systems and data-intensive applications
Real-Time Event Processing Engine
High-performance event processing system sustaining 10K+ events/sec with sub-50ms latency. Implements worker coordination, retry logic, and memory-efficient data structures to maintain correctness under load.
Causal MMD
Scalable Flask application integrating 1.03M+ records with ensemble classifiers, achieving 92% accuracy for real-time disaster response coordination. Optimized with async I/O and batch processing, scaling throughput to 10K+ points/min. Built thread-safe REST API with versioned endpoints, containerized with Docker and deployed via CI/CD with 95%+ test coverage.
PUBLICATION
An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
Saketh Vishnubhatla, Alimohammad Beigi, Rui Heng Foo, Umang Goel, Ujun Jeong, Bohan Jiang, Adrienne Raglin, Huan Liu
ACM Digital Library — dl.acm.orgCURRENTLY
High-Perf Networking Library
Async I/O event loop in C++ using io_uring for zero-copy networking, targeting sub-microsecond latency for financial data feeds.
Distributed Storage Internals
Deep-diving into LSM-tree design, compaction strategies, and write-ahead logging — inspired by RocksDB and LevelDB internals.
Systems Programming Papers
Working through "Designing Data-Intensive Applications" and recent VLDB/OSDI papers on distributed query execution and storage engines.
CONTACT
Graduating May 2026 · Actively seeking full-time Software Engineering roles.
Passionate about building high-performance, scalable storage and data systems.