Shipping mission-critical features to enable resilient healthcare supply chain operations in hospital systems across the United States.

Developed a multitude of highly customizable mission critical products, services, and pipelines in concert with design, product, and engineering.

Led engineering-wide polyrepo to monorepo migration initiative for several of our core services.

Implemented dry run capabilities to client specific pipelines to empower client facing staff, reduce deployment risk, and eliminate rework.

Reduced automated testing pipeline run times by 95% (~20 minutes -> 1 minute) and created tooling to more easily run tests locally in less than 5 seconds.

Introduced Swagger documentation capabilities to assist front end development and improve backend API documentation – to date ~50 of our endpoints are interactively documented in Swagger and growing.

Authored extensive onboarding docs and sample PRs, reducing ramp-up time for engineers and enabling CX teammates to debug independently.

Actively involved in technical hiring, candidate interviews, and backend guild initiatives to improve dev experience across pods.

Regularly support client calls to clarify technical functionality, custom automation logic, and assess new feature feasibility.

Partnered closely with product, CX, and design to deliver data-driven features aligned with real-world supply chain challenges.

Enhanced data integrity checks by leading the design and deployment of a real-time edit check system for an oncology-focused EDC platform, enabling immediate data accuracy feedback and user interventions. Collaborated with product management and design teams to ensure the system met client needs and provided optimal user experience. Developed a customizable platform for client-facing teams to build client-specific edit checks. Implemented advanced monitoring with Datadog and structured logging, and upgraded infrastructure from ELB to ALB for improved resource isolation. Integrated real-time notifications into the React-based UI using WebSockets, significantly enhancing user engagement and reducing error resolution times.

Resolved critical scalability issues in a data delivery pipeline handling clinical trials, as part of a targeted internal scalability task force. Diagnosed and overhauled a failing Node.js pipeline initially comprising thousands of lines of poorly structured code, improving code modularity and readability. Transitioned processing from a single overloaded Lambda function to a robust EC2-based scheduled task system, utilizing AWS Step Functions for efficient data segmentation and orchestration. Implemented a strategy for memory management by processing subsets of patient data, significantly reducing timeouts and memory constraints. Achieved reliable conversion and delivery of clinical trial data into SDTM format across JSON, CSV, and XPT formats, culminating in automated outputs to S3 for a 75,000 subject study.

Engineered and deployed a full-stack data exploration application to empower client-facing teams with real-time insights into oncology patient datasets. Collaborated with product and design teams to create a user-friendly interface in React, with a Python/Flask backend. Integrated complex querying capabilities into the Snowflake database, allowing non-technical users to dynamically filter patient populations based on criteria such as cancer type, age, treatment history, and geographical location. Containerized the solution and deployed on AWS EC2, streamlining the generation and export of custom reports. This tool significantly enhanced data accessibility, enabling clients to make informed decisions about dataset utility for their research needs.

Mentored new hires and managed projects for summer intern and spring apprentice

Developed and optimized a data integration pipeline for merging oncology patient datasets with healthcare claims data, enhancing the comprehensiveness of cancer patient profiles. Collaborated closely with internal client management teams and external partners, including Datavant, to align project objectives and ensure seamless integration. Automated the anonymization and merging process by containerizing the Datavant CLI application within an Airflow pipeline, replacing previous manual and local data handling methods. Implemented efficient data upload workflows and secure data sharing via S3, with subsequent processing and storage in Snowflake. This transformation significantly reduced manual labor and improved data integrity and accessibility for research purposes across multiple partner sites.