Daniel – Python, React, Django

MVP provides medical practices with analytic software data consolidation tools and web-based visualizations that simplify understanding the business’ performance trends within one single multi-purpose dashboard from a variety of applications in order to streamline the decision-making process for optometry clinics.

• Developed a prototype from the ground up, starting from the conceptual "0" stage to a functional "1" state;
• Engineered a Django backend from scratch to integrate with QuickBooks and Google Drive, alongside a PostgreSQL backend;
• Processed financial PDF documents to extract key performance indicators (KPIs), subsequently updating Google Sheets files and PostgreSQL databases;
• Developed a dashboard using Charts.js to access and update information from the PostgreSQL backend, facilitating data-driven decision-making;
• Automated background task processing to ensure consistent file processing in Google Drive;
• Collaborated directly with the CEO to ensure timely completion of deliverables and adherence to MVP/prototype specifications.

The platform's main goal is to enhance an individual’s mental health by providing on-demand support from a clinically trained empathic AI system focused on emotional exploration of the root causes of distress and suffering.

Among Daniel's achievements are the following:

• Developed a prototype for a digital therapeutic AI assistant using OpenAI's API and Python libraries, in collaboration with an AI researcher;
• Designed the backend architecture using hexagonal and domain-driven design concepts;
• Integrated the AI assistant prototype into an MVP of a digital therapeutic app, connecting it with the ReactJS frontend via a Django-Rest-Framework API;
• Implemented Auth0 authentication, including social logins and passwordless authentication, to enhance app security;
• Established user engagement features such as a mailing list and user sign-up and created an admin site for management purposes;
• Automated deployment processes using Google Cloud Platform services like App Engine, Cloud SQL, and Cloud Storage;
• Installed Sentry for error monitoring and set up Slack integration for real-time reporting of issues;
• Managed Google ad campaigns to promote the digital therapeutic app effectively;
• Configured Google Analytics, Google Tag Manager and implemented SEO best practices to optimize the app's online visibility and performance.

Software designed to automate the tip distribution process for restaurants, facilitating nightly or weekly tip-outs.

Daniel carried out the following tasks, among others:

• Designed, developed, and tested API endpoints for a Tip Calculator project aimed at automating tip-out processes for restaurants;
• Managed the project from inception to deployment, achieving production status with Earl’s restaurants across Canada;
• Utilized a FastAPI/Vue/PostgreSQL/Docker stack within a microservices architecture;
• Integrated with external APIs from various Point Of Sale (POS) services within the hospitality industry to process and synchronize multiple data feeds;
• Implemented and managed Docker containers to enhance DevOps practices;
• Developed a subscription and invoicing system leveraging Stripe’s API;
• Provided training to Sales and Marketing teams on operational and onboarding processes;
• Collaborated with Client Services and Sales teams to introduce the new product to current and potential clients, demonstrating its potential to streamline bookkeeping processes;
• Designed and developed Vue components for a public-facing application;
• Designed and maintained SendGrid email templates and campaigns.

Internal platform tailored for analytics and video access, catering to players, coaches, and scouts within an NHL team.

Daniel has succeeded in the following tasks:

• Designed, developed, and supported new and existing tools within TML’s Django platform;
• Handled the ingestion of various data feeds, including player tracking data, scouting reports, and video content;
• Transformed NHL, Sportlogiq, and optical tracking data into actionable metrics and presentations;
• Managed the extraction and attachment of play-by-play clips from lengthy game videos;
• Devised algorithms to extract valuable insights from extensive datasets, including new CV play-by-play data;
• Created tools enabling colleagues to explore, visualize, and manipulate data effectively;
• Orchestrated background tasks using Celery on Heroku;
• Conducted testing using Django’s built-in UnitTest framework;
• Oversaw configuration and maintenance of cloud infrastructure on Heroku and AWS;
• Collaborated with non-developer colleagues to leverage platform benefits;
• Analyzed hockey data and conveyed findings to team members;
• Participated in collaborative research initiatives.

This was a large suite of software that aided and automated the betting and basketball analytics processes for an NBA gambling corporation.

• Developed, designed, and maintained ETL data pipeline for large basketball data sets;
• Built backend API using Django REST Framework for both internal and external users;
• Created the front end using vanilla JS and current ES6 standards;
• Set up and maintained production infrastructure on Amazon AWS platform (EC2, RDS, SQS, S3);
• Developed internal data reports and visualizations using traditional boxscore and optical data;
• Created and maintained data pipelines for xml/json feeds;
• Processed feeds into MySQL databases/tables;
• Optimized data pipelines using Redis cache;
• Automated scraping of unformatted data;
• Built user interfaces to identify various types of basketball plays from game videos in order to train machine learning algorithms to recognize these events from the raw tracking data;
• Automated various internal tasks using a farm of Selenium-Webdriver instances (up to 200 firefoxes);
• Worked on a small remote team of less than 10 developers/analysts;
• Installed SSL Certificates using Lets Encrypt;
• Operated a farm of 20 socks5 proxies.