- Develop multiple customer facing web games with thousands of daily players- Produced the 3rd most visited gaming website in Spain- Build using serverless infrastructure, React, Svelte, Typescript and other web technologies- Developed multiple data science based offerings in the AWS marketplace with hundreds of paying customers

- Worked with some of the most important Google Ads third-party partners triaging issues, handling interactions with customer facing and internal engineering teams, proposed changes to the API working with product managers based on customer feedback- Managed the Google Ads API Ruby client library. Handled releases, examples, issues, examples and more- Managed the release of documentation for releases of the Google Ads API- Prototyped experiments to improve the experience of Google Ads API users: https://github.com/danielfrg/adsctl and https://github.com/danielfrg/terraform-provider-googleads)-Proposed and implemented changes to the developer experience for conversion management- POC for multiple areas of the API working with Engineers and Product Managers to improve the experience of users20% projects:- Developed multiple LLM demos for Google Cloud Next: https://llmops-demos-frg.web.app and https://ask-dora.web.app

- Worked developing a new enterprise data platform based Apache Arrow- Worked with Product Management and other engineering teams to create a roadmap for our product- Tested and validated Theseus a GPU SQL engine using industry standard tooling (TPC-H TPC-DS)- Improved and created new CI/CD pipelines for different projects of the company- Discovered top friction points of existing products and prioritized a roadmap to improve the software

[Company was acquired by DataRobot]- Designed and implemented data pipelines using Apache Kafka to feed data and monitor metrics of algorithms deployed on the platform- Implemented integration between MLFlow and Algorithmia- Worked with customers to implement and maintained multiple executing environments as Docker images- Worked with customers to integrate the Algorithmia Platform with third-party tools for metrics monitoring and CI/CD pipelines- Implemented state of the art algorithms in the Algorithmia Platform- Wrote documentation and blog posts about new features of the platform

- Worked with customers and the sales team to deliver POC architectures and worked with the team to implement these architectures- Specialized in enterprise DevOps as well as product integrations with Python, Kubernetes, Slurm, and Spark- Integrated enterprise data science products in production environments with Python and R- Automated multiple systems to improve the delivery of customer-facing products- Worked closely with support, engineer, and QA teams to automate testing of data science products in multiple enterprise environments- Wrote multiple technical documentation for multiple pieces of the products including cloud integrations, authentication, and other enterprise tools such as Kubernetes and Spark

- Worked on collecting user and field input to prioritize it into actionable roadmap themes by writing clear, testable user stories and epics while working very closely with the engineering team- Focused on the data scientist experience especially around Python and R users and tooling and integrations like Hadoop, Spark, Kubernetes- Built multiple Machine Learning customer-facing demos, assets, applications, and dashboards for different industries using scikit-learn, Tensorflow, Jupyter, and JavaScript- Work close to the engineering team to ensure the delivered product meets user needs - Built multiple prototypes for the platform to validate new features including Model Management and Data Catalog. These prototypes were functional and acted as a way to show customers possible implementations of upcoming features- Worked close with sales and implementation teams when they need technical and non-technical support, present product features and technical direction to SE teams, and write technical content for end-users and the field- Created multiple marketing materials including and wrote documentation for multiple areas of the product being considered an SME- Part of the team who got the product into the Gartner Magic Quadrant for two years in a row, making significant progress the second year. I was in charge of writing and recording the demos for these certifications

I was part of the team that re-architected and consolidated the multiple products into one integrated data science platform. The new architecture of the platform allowed the product to ship new features faster, improved productivity, and shorten the implementation time from months to days helping reduce the sales cycle.- Built the initial prototype of the platform to test most of the technologies that ended up being part of the final cloud-native infrastructure based as Kubernetes and Docker- After showing this prototype to customers we decided the company decided to re-architect and consolidate products into one- Trained the engineering team in the basic usage of Kubernetes- Evaluated the usage of technologies such as Keycloak for authentication and authorization and Minio for storage- Coded multiple services and APIs of the platform using Python- I was in charge of writing the initial installation code for the new platform and supported implementation and sales engineers- Made partnerships decision on key areas of the product such as licensing, packaging and installation

- Worked on new products that intersected the work of data science and data engineering making big data tooling such as Spark easier to use for traditional data scientists - Developed a tool for dynamic distribution of Python libraries to distributed engines such as Hadoop and MPI clusters- Developed a tool to reduce the implementation time of the enterprise products from multiple months to a couple of days- Supported and worked directly with implementation and sales engineers to gather feedback on the tools we were building

- Developed the configuration management infrastructure by evaluating multiple options like Chef, Puppet, and Saltstack, choosing the latter- Trained trained the development team in the use of multiple tools such as Vagrant and Saltstack to have a standardized development environment- Developed a tool for ingesting data into HBase, this tool was connected to the different pieces of the infrastructure such as RabbitMQ and a multiple Java REST APIs- Installed, configured, and maintained the Jenkins CI server and trained the team on its use for continuous integration and continuous deployment using Saltstack- Developed a framework for running batch data processing jobs in the AWS using open source tools such as Luigi, RabbitMQ, and Saltstack. The main objective was to provide the users an easy, intuitive, and fast development framework

- Coded backend and frontend solutions to handle data ingestion- Collaborated in the development of a crawling infrastructure based on Apache Nutch and Apache Hadoop- In charge of exposing a near-real time prediction of multiple machine learning algorithms via a Java REST API using Play framework connected to a Python ZMQ endpoint that served the ML model

The competition required the construction of a semi-autonomous robot with wireless communication and sampling capability exceeding 10 kg of lunar regolith.- Lead software engineer of a team of 23 students- Coded for the communication between the robot and the control computer- Coded the robot movement (Engines, Actuators, Encoders)- Ninth place over 70 international teams- Third Place on the Slide Presentation and Demonstration category- First Place on the Best use of Social Media category