Threat Intelligence Team

• Owned end-to-end development of a threat intelligence product (0→1), driving customer discovery, feature design, and iteration through direct customer calls, demos, and feedback loops

• Built systems for analyzing and visualizing relationships between security threats, modeling entities and connections as graph structures to enable detection of coordinated attack patterns

• Developed timeline-based views by aggregating and ordering event data, allowing users to trace how threats evolve over time

• Designed backend workflows to compute relationships between entities (e.g., shared infrastructure or behavioral similarities), improving visibility into attack campaigns

• Designed how campaign data is translated into clear, user-facing summaries, determining key inputs and structuring outputs to effectively communicate patterns, risks, and insights

• Enhanced performance of data-heavy workflows through query optimization and caching strategies to support interactive exploration

Core Team

• Defined and implemented rule-based and heuristic filtering logic to identify and exclude non-malicious data, ensuring detection systems focused on meaningful threats

• Developed a filtering system to prevent benign or customer-owned content from entering detection pipelines, leveraging pattern matching, metadata validation, and allowlist/denylist strategies to reduce false positives

• Built long-term data storage (cold storage) infrastructure to support reliable retention and retrieval of historical data, enabling access to past events without impacting primary system performance

Human Risk Management Team

• Led development of an AI-powered content generation system, using LLM-based workflows to generate and structure security training content at scale

• Designed workflows to transform unstructured customer requests into structured training outputs using templating, AI generation, and validation

• Implemented core ETL mapping, enabling data extraction from custom sources and transformation for integration with any custom destination

• Contributed to backend development, MySQL and Protocol Buffer design, and React state management, leading to a promotion within nine months

• Engineered a smooth transition from AWS to GCP, enabling support for both platforms to meet customer preferences for data storage location

• Resolved critical user-facing bugs by adding and analyzing tracing in Grafana Tempo, improving application stability and user experience

• Optimized database queries to improve pagination load times by 700% and decreased the wait time for customers on highly trafficked pages

• Led a team of two and designed the technical specification for the project by addressing user complaints and critical edge cases

• Created a machine learning text classifier on the Scratch 3.0 platform by using Tensorflow for the Amazon Future Engineers program

• Conducted front-end work using HTML on the Scratch 3.0 platform to make it simple for young users to navigate

• Taught and utilized the text classifier in a middle school curriculum to explore AI and its ethics for the Amazon Future Engineers program

• Taught and utilized the text classifier at a Women in STEM week for a high school camp based in Mexico

• Used Lightstep, Grafana, Nomad, and Sentry to triage alerts and create fixes for cache failures, stuck flows, and UI errors

• Created event handlers in Java that listen for specified changes and publish Protobuf messages to Kafka topics

• Designed custom Apache Airflow tasks and DAGs to export, process, and upsert data between databases