Lucero Berrini Gaston Agustin

• Implement the backend for a ticketing platform using Python and FastAPI, using Pydantic for schema validation to ensure data integrity and consistency across the application. Utilized PostgreSQL with SQLAlchemy ORM for database management, ensuring efficient data handling. • Developed automated tests, using Behave for BDD integration testing with selenium for UI automated tests and pytest for unit testing. • Used Docker Compose for the development environment, reducing setup time. • Designed and implemented the frontend using React with TypeScript, creating a responsive user interface. • Implemented frontend component tests using vitest.

• Implement futures trading strategies using Python freqtrade framework. Working with big data libraries like pandas and numpy. • Implement ML Models for predicting price movements in cryptos assets. Working with libraries like Sklearn and CUML for GPU usage. • Developed FE for Chart and trades visualization using Typescript, HTML, SCSS. Using Vite as building tool, and amCharts 5 stock chart.

• Designed and implemented a cash advance platform that supports ACH cash advances and repayments. Managing user subscriptions and monthly invoices. Utilizing technologies such as Solid, Plaid, Brex and Stripe. The platform allows for the issuance of debit cards with Real-Time Authorization (RTA) using Plaid and Solid, and enables Debit Push cash advances to Cards using Solid, Plaid, and Tabapay. Allow cash advance using AWS Incentive Gift cards. Successfully processed around 30,000 cash advances montlhy, with an average of $50 per cash advance, resulting in approximately $1.5 million in cash advances monthly. • Established development guidelines and best practices for the team, promoting a productive and efficient work environment. • Implemented the platform using Python with SQLAlchemy and Tornado, PostgreSQL DB, Docker, and various AWS services (QLDB Ledger, IAM, Lambda, ECS, ECR, Dynamo, Dynamo TTL, SQS, RDS, AWS Secrets Manager, Parameter Store, Cloudwatch logs, Cloudwatch events). Infrastructure was managed using terraform, horizontal auto scale based on load system and Github actions were used for CI/CD. • Set up monitoring and alerting using Datadog, New Relic, and Grafana to ensure platform stability and performance. Employed Python Behave for integration testing, ensuring the reliability of the system under different scenarios. Using customerIO for notifications and one signal for tracing user flows.