Afsana Rahman headshot

Afsana Rahman

Third-year Financial Engineering student at Columbia University exploring quantitative systems and product thinking.
afsana.rahman@columbia.edu

Skills & Experience Explore Photography

Work I’ve Done

  1. amazon — built and operated reliability and monitoring systems for EKS-backed distributed services, implementing health checks and incident-response tooling to improve system availability
  2. bloomberg — developed real-time data pipelines using Apache Flink, working on stream processing, event-time analytics, and high-throughput distributed data systems
  3. nasa — contributed to product strategy and technical planning for a swarm robotics system designed for planetary exploration
  4. exiger — analyzed and resolved production issues across backend services and data pipelines, built monitoring dashboards and automated workflows to surface failure patterns and reduce recurring incidents
  5. digital campaigns @ columbia — executed marketing, content, and growth initiatives across the bulletin, undergraduate student life, nsop, and multiple student organizations

Work I’m Doing

  1. ieor department @ columbia — lead product engineer building a full-stack academic planning system, implementing DAG-based prerequisite resolution, constraint satisfaction logic, and rule-driven plan over institutional SIS data
  2. master’s fe practitioners seminar — teaching assistant supporting course operations, instruction, and student learning
  3. product management fellowship — faciliating and developing product thinking through hands-on scoping, prioritization, and real-world case work
  4. core podcast — founding member shaping content direction, production, and storytelling for student entrepreneurship
  5. content creation @ columbia — running social media and digital content for columbia data science society and club bangla, focusing on growth, engagement, and brand consistency

Work I’m Exploring

  1. how machine learning systems move from models to production, especially the infrastructure, data pipelines, and evaluation loops required to deploy ML reliably at scale
  2. quantitative backend systems, where mathematical models, optimization routines, and simulations are implemented as production services
  3. the boundary between algorithms and systems, including how optimization, graph-based reasoning, and heuristics are embedded into real backend services
  4. building internal tools that support engineers during incidents, from debugging workflows to clearer system visibility
  5. data-intensive products in finance and infrastructure, where correctness, latency, and reliability directly shape real-world decision-making
  6. digital platforms and content systems that center accessibility, ethics, and responsible technology design

Skills I Use

  1. writing production-grade backend code in python, java, and sql to move systems from prototype to production
  2. designing and evolving backend services and apis thinking through schema design, service boundaries, and constraints
  3. building and maintaining real-time and distributed data systems , including streaming pipelines, event-time logic, and failure handling
  4. deploying and running systems in the cloud using AWS and kubernetes, with attention to reliability, cost, and scalability
  5. defining and tracking kpis and success metrics to guide prioritization, evaluate impact, and iterate on technical and product decisions
  6. running digital platforms and growth systems, using analytics to iterate on messaging, engagement, and community outcomes

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