Software Engineer Staff

Michael Pham

Staff Software Engineer

Merced, CA 95348 972-***-**** *******.***.*******@*****.*** SUMMARY

Full-stack engineer with 10+ years of strong experience in cloud platforms, enterprise systems, and real-time data pipelines. Skilled in building secure, scalable applications across GCP and AWS, with hands-on expertise in backend services, frontend development, data engineering, and applied machine learning. SKILLS

Programming Languages: HTML, CSS, Java, Python, Go, C#, JavaScript, TypeScript, Ruby, Liquid, SQL, C/C++ Backend: FastAPI, Flask, Django, Jin, Spring Boot, Node.js, Ruby on Rails, GraphQL, RESTful APIs, WebSocket Frontend: React, Angular, Next.js, Redux, Backbone.js, Bootstrap, Tailwind CSS, Material Design Distributed Systems: Microservices, Event-Driven Architecture, Real-time Processing, Kafka, RabbitMQ AI & ML: TensorFlow, Vertex AI, OpenAI, LLM, Langchain, Embeddings, RAG, Generative AI, Vector Database Databases: MySQL, PostgreSQL, Firestore, NoSQL, DynamoDB, MongoDB, RDS Cloud Services: GCP (BigQuery, Cloud Dataflow, Cloud Functions, Cloud Storage, Pub/Sub, Firestore, Cloud Build, Cloud Monitoring), AWS (Lambda, Kinesis, SQS/SNS, DynamoDB, QuickSight, Comprehend Medical), Azure Deployment & CI/CD: Apache, Kubernetes, Docker, Terraform, Jenkins, Cloud Build, Git, Github Actions Methodologies & Practices: Agile, Scrum, Unit Testing, A/B Testing, Jest, Mocha, JUnit, Pytest, CI/CD Others: AirFlow, HIPAA Compliance, Encryption, Access Controls, Health Data Interoperability PROFESSIONAL EXPERIENCE

Google

Staff Software Engineer Feb 2020- Present

Developed fullstack clinical platforms for Google Care Studio with event-driven architecture and real-time data pipelines, enabling seamless patient data integration and supporting millions of health records processed daily across healthcare providers.

Architected and developed robust backend microservices, including a high-performance clinical search and analytics engine, using Java / Spring Boot, Python, and Go, ensuring secure, scalable, and reliable APIs for real-time patient record querying and AI-driven insights in Google Care Studio.

Designed and developed a real-time patient health dashboard interface using React and Redux, integrating WebSocket streams for live data feeds from Google Fitbit and EMR systems, resulting in a 35% increase in clinician engagement and accelerated feature iteration based on continuous user feedback from pilot programs.

Optimized database performance for PostgreSQL and Firestore by refactoring complex queries and implementing advanced indexing strategies, achieving a 50% reduction in query execution time for population health analytics.

Designed and implemented data engineering pipelines using BigQuery, Cloud Dataflow, and Apache Airflow to handle real-time EHR and wearable data streams, optimizing ETL workflows and monitoring with Cloud Monitoring, reducing data processing latency by 40%.

Implemented serverless solutions on GCP, leveraging Cloud Functions, Cloud Storage for data archival, Pub/Sub for event notifications, and Firestore for low-latency state management, automating real-time health event processing and alerting to reduce system latency by 30% in Google Fitbit integrations.

Implemented secure AI model integrations using TensorFlow and Vertex AI, building cross-system data harmonization solutions with Java and Python, leveraging federated learning and differential privacy to enhance security and enable seamless AI-powered predictions across Google Care Studio’s decentralized health platforms.

Created and maintained Google Health APIs and SDKs in multiple languages (Java, Python, JavaScript), enabling third-party developers and healthcare partners to integrate with patient data systems and Fitbit wellness features.

Established comprehensive testing frameworks, including Jest, Mocha, and JUnit, and developed automated CI/CD pipelines with Kubernetes and Cloud Build, ensuring robust fullstack integration testing, improving code quality, and reducing deployment times by 45% for seamless feature delivery across health platforms.

Implemented comprehensive unit and integration tests using Jest, JUnit, and Pytest, achieving 95% code coverage, ensuring robust backend integration testing, improving code quality, reducing deployment times by 45% for seamless feature delivery across clinical and wearable platforms, and significantly minimizing production defects in sensitive health environments.

Mentored over 5 junior engineers and fostered cross-functional collaboration between product, clinical, and DevOps teams within Agile workflows, improving team productivity by 35% and accelerating delivery cycles for complex AI-enhanced health features in Google Care Studio and Fitbit. Amazon

Senior Software Engineer Sep 2017- Jan 2020

Developed an AI-powered triage and analytics system for One Medical, automating symptom classification and enhancing medical call center efficiency, achieving 92% triage accuracy and 25% backlog reduction across pilot clinics.

Built serverless backend services using Java and Python with AWS Lambda, integrating SQS/SNS for fault- tolerant event processing, reducing system latency by 30% for patient request handling.

Integrated AWS Comprehend Medical for NLP-driven extraction of symptoms and ICD/CPT codes, improving transcription accuracy for reliable triage outputs.

Designed real-time clinician dashboards using QuickSight and React, providing actionable insights into triage accuracy and workload, boosting throughput by 30% and enhancing user engagement.

Engineered Kinesis-based data pipelines to process transcribed patient records in real time, optimizing ETL workflows to support scalable analytics and reduce processing delays by 25%.

Implemented AI-driven triage logic with Comprehend Medical, enabling automated symptom classification and supporting early intervention, achieving sub-second response times for recommendations.

Established automated CI/CD pipelines using Terraform and Jenkins, incorporating JUnit and Pytest for 90% code coverage, reducing deployment times by 40% and minimizing production defects.

Optimized DynamoDB partitioning and indexing strategies to handle high-volume patient data, mitigating hot partition issues and ensuring sub-second latency for triage queries.

Ensured HIPAA-compliant data pipelines by implementing encryption and access controls in Lambda and DynamoDB, safeguarding sensitive patient information across distributed systems.

Collaborated with data scientists, clinical stakeholders, and DevOps teams in Agile workflows, refining AI models and dashboard UX, accelerating feature delivery by 25%. Shopify

Software Engineer Jul 2014- Aug 2017

Developed ecommerce storefront features in Ruby on Rails and Liquid templates, powering merchant dashboards for 100k+ stores.

Built frontend components with JavaScript, React, and Backbone.js, improving performance of Shopify Admin pages.

Designed APIs in Node.js and GraphQL for checkout, inventory sync, and discount systems.

Contributed to early container adoption with Docker, improving deployment workflows for merchant services.

Collaborated with product managers and mentored junior engineers on Ruby on Rails best practices and scalable architecture.

EDUCATION

Colgate University

BS in Computer Science Aug 2010 – May 2014

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