My current role is very focused on a new AI team within the group that seeks to generate credits and verify users based on their interactions, logs and how they have used their score in the past. The first projects I am involved in are based on creating automatic dashboards that allow visualizing the user event funnel to verify their behavior and create campaigns that improve conversion. The other projects are to generate Bots and Agents that internally allow generating data and predictive behavior models to generate new clients and new products, as well as improve current products and generate a positive impact on the lifecycle of the credits, cards and products that the group has. We use the full power of the cloud with AWS (EKS, SQS, S3, EC2, Lambda, RabbitMQ, Dataloader, etc.), for the user interfaces we develop microfronts with React and Redux which connect the Backend in Python and Java to generate and connect the trained and untrained models. We use automated deployment and best practices with PEP8, tackling CI/CD tasks with Jenkins and Jira.

My involvement in the SNG project consisted of developing a platform for the National Customs Agency. This involved developing all the platform's backend services, creating and developing the entire database architecture, and integrating services with the project. I implemented scripts and pipelines for Microsoft Azure tools such as Azure DevOps, AKS, ACI, API Management, Pipelines, CLI, SDKs, and Cosmos DB. I developed and integrated all the connections the project required, using a technology stack with Python, Django, PostgreSQL, SQL Server, Git, Docker, and K8S. I also added a log recovery structure to have a tracking system in case the server had a problem or whenever there was any unusual activity on the server. One of my last tasks was to implement an ETL process to cleanse and load data from the different databases we integrated into the project, also adding an automatic daily update step with tasks and chrons.

My experience in financial environments has been brief but very satisfactory, leading automation projects for calculation tools for sales agents and also managing integrations of accounting and finance tools such as SAP and other ERPs. I have even had to lead projects to migrate web services and servers from one programming language to another, improving their performance in the process.

My last position at Rappi was as a Backend Engineer in the Turbo team using JavaScript, TypeScript, NodeJS, Python and GO for the development of new projects (mostly microservices), implementing new scalable technical solutions to different areas; within my responsibilities was the planning and creation of the architecture of the new microservices, starting with the modeling of the databases, implementation of the solution in code, creation of unit tests, implementing automatic actions with Jenkins and GitHub Actions. In most projects I have used AWS cloud tools such as: S3 Buckets, EC2, RDS, and Lambda for almost 5 years. Speaking of Snowflake: I developed and optimized complex SQL queries in Snowflake, improving reporting performance by 80%; I also implemented ETL processes using Snowflake, resulting in a reduction in data processing time by 60%; I participated in the migration of data to the Snowflake platform, ensuring the integrity and availability of information; I designed and built data models in Snowflake to support business analysis initiatives, and finally I connected Snowflake with data visualization tools such as Power BI to create interactive dashboards. I also have knowledge using Git, Redis, Docker, K8S, Airflow and Kafka to work with the microservices environment and technologies. These last tasks focused on improving the performance of the web pages and improving the scalability of the data by improving and implementing new strategies for loading and reading the databases, while implementing caching strategies such as Redis and versioning and fluidity of queries with Snowflake, Fivetran and using Linux and Unix for so many different projects. In many projects that I built from scratch with my team, I had to implement Machine Learning solutions, using Python, Spark, ScikitLearn and Tensorflow. The main projects were to create a predictive model for stock and product recommendations, focused on Strategic Brands and Stores. I worked closely with Product and Business to coordinate campaigns, promotions and integrations that focused the model on improving the GMV of said Brands and Products in stores.