Will
Flowers

Player-coach lead for the engineering team, and hands-on in the highest-risk systems. Team: 5–6 engineers

App Store ↗Google Play ↗

• Co-built and shipped the iOS and Android apps (Expo/React Native) through the App Store and Google Play: account creation, eSIM purchase and installation, number purchase and provisioning, porting, and in-app calling and SMS.
• Worked on multi-provider telephony across Telnyx SIP, DirectConnect, and CloudNumbering: provider abstraction and failover, number provisioning, SMS, calling, and country-specific porting workflows.
• Helped bring real-time mobile voice to production, working through SMS delivery, synchronization, push notifications, and background-app reliability.
• Led the Flask-to-FastAPI migration and Terraform-based infrastructure work, and established automated test execution in CI.
• Hardened the backend: authentication and authorization, secrets management, webhook verification, rate limiting, encryption, and GDPR privacy controls.
• Set technical direction through design and code reviews, regular 1:1s, release ownership, and an explicit testing culture.
• Introduced BMAD and spec-driven delivery, and built the team's AI-agent pipeline that drafts, implements, and quality-gates sprint stories with people reviewing every step.
• Led PostHog instrumentation across calls, SMS, authentication, KYC, purchases, and eSIM events to give the team a product and reliability measurement foundation.

Tools Python, FastAPI, Flask, TypeScript, React Native, Expo, SIP/VoIP, PostgreSQL, Supabase, Firebase, GCP (Cloud Run, App Engine), Terraform, GitHub Actions, PostHog, Stripe

Platform engineer on the data-science platform team. Team: 12

• Worked hundreds of support, feature, and integration requests from internal users, and set up onboarding, permissions, and data and volume transfers for the new platform.
• Got SolasAI, a third-party risk and equity-metrics vendor, running inside Databricks, unblocking a key internal business partner that funded the team's work.
• Restored an Ansible build process that had been out of commission for eight months, smoothing the move away from Domino.
• Fixed bugs in a critical legacy Scala codebase that most of the team couldn't get to build.
• Managed the AWS infrastructure behind platform monitoring and uptime, and contributed to integrating an internal modeling pipeline with Databricks Lakehouse Monitoring.
• Regularly mentored teammates on problems where I had more depth.

Tools AWS (EC2, Lambda, CloudWatch, RDS, Secrets Manager, S3), Ansible, Bash, Databricks, Domino Data Labs, GitHub Actions, Jenkins, Kubernetes, Python, Scala, Spark, Terraform

Independent consultant, owning each engagement end to end. Team: Client teams of up to 5

Tools Python, Flask, React, AWS (Amplify, Certificate Manager, Cognito, Elastic Beanstalk, IAM, S3), GitHub Actions, Gunicorn

Owned the pipeline from raw ingestion to reporting. Team: 20

• Books were taking months to close. I built ingestion for the two largest customers' disparate order data, and designed reusable classes so new customers could onboard easily.
• Owned data from raw ingestion to Sisense dashboards through batch ETL jobs with 95%+ unit-test coverage, integrated data-quality assertions, and 95%+ uptime.
• Contributed to the core product backend (REST APIs and relational data work) through stories and code and design reviews.
• Deployed jobs to AWS with Terraform.

Tools Python, Django, Django REST Framework, PostgreSQL, Pandas, AWS S3, Terraform, GitLab, OAuth2, SFTP, Sisense

Technical leader on a 20-person cross-departmental program, especially abstraction, configuration, and testing. Team: 20 (propensity program), 4 (analytics)

• Built a home-grown propensity-modeling system (mostly XGBoost) predicting customer behavior from internal and external data, giving Sales and Marketing actionable insight.
• Iterated on and regularly deployed 50+ models within a suite of 200+, with accuracy gains of 50%+ on some. Results piloted to 400+ users with a statistically significant improvement over previous solutions.
• Led the move from Domino Data Labs' proprietary version control to an internal GitLab, with tutorials for about 20 people: branching, peer review, change history, and releases.
• Stood up CI with GitLab CI and Domino, and bolted Great Expectations and MLflow onto the platform for experiment management and automated data-quality testing.
• Introduced configuration files and a normalized data model, which increased code reuse and unified the underlying sources of truth.
• Moved large data artifacts out of the repo into S3, speeding up container boot times; managed the team's Docker and Linux builds.
• Earlier, built the data-processing layer and API behind a Marketing and Sales dashboard reaching 1,000+ internal users: 12x faster than the original algorithm, scalable on OpenShift, with 85% test coverage in CI. It launched on time, within a few months of my start.

Tools Python, SQL, XGBoost, scikit-learn, SHAP, Ray Tune, Pandas, Airflow, MLflow, Great Expectations, Domino Data Labs, AWS (S3, Redshift), GitLab, Flask, Celery, MongoDB, MySQL, OpenShift

Back-end lead while traveling and working remotely. Team: 7

• Learned Ruby and Rails and was contributing within weeks.
• Automated the deployment pipeline and managed DevOps, making releases faster and less error-prone.
• Led training and onboarding sessions and interviewed candidates for hire.

Tools Ruby on Rails, PostgreSQL, Heroku, AWS S3, JavaScript, jQuery

Solution Validation Services team. Team: About 30

• Contributed to a framework that dynamically automates testing of networks and topologies.
• Helped define the Git workflow, and implemented Git hooks and CI for client- and server-side QA.
• Onboarded eight new teammates as the team grew from 12 to 30 in a few months, and started a biweekly lunch skill-share.

Tools Python 3, Docker, GitLab, REST, Cisco L2/L3 networking

Backend developer. Team: 2, plus 4 client-side

• Built a weekly pipeline parsing 75+ GB of provider XML into CSV and PostgreSQL, served as JSON through a Django REST API.
• Added unit tests, Python 3 compatibility, and CLI tools for public Medicare provider data.
• Set up Jenkins CI for existing Python and Django projects, and JSON Schema validation for the FHIR healthcare data format.

Tools Python, Django, PostgreSQL, Jenkins, FHIR, AWS