Maths | Data/ML | Fullstack | Nix/NixOS
"Basically a wizard"
Employed at Tamtam to build excellent software
- LinkedIn, I write about stuff: https://www.linkedin.com/in/guillaume-desforges
- GitHub, I make some stuff: https://github.com/GuillaumeDesforges/
I'm a freelancer, contact me by email. Currently not available for freelance.
I have taught at French universities the following course.
Don't hesitate to reach out if you want to see the teaching material - if it's not public on GitHub already.
Lectures given
- "Python"
- "Techniques de dévelopment logiciel"
- "Techniques de dévelopment logiciel"
- 1-day course and workshop to version control with git
- "Big Data"
- "Big Data"
- "Machine Learning: Supervised Learning"
- "Scraping and data cleaning"
- "Machine Learning: Supervised Learning"
- "Machine Learning: Supervised Learning"
- full-stack development of features, from product to frontend, backend, and ops
- product analytics
Past experiences
Tweag, a Modus Create company
- consultancy: requirement gathering, delivery, communication
- leadership: coaching, project management, group roadmap
- growth: hiring (interviews), marketing (speaker, blog editor), sales (solution design)
- fullstack web development, embed LLM
- build foundation of a marketing engine that uses ML
- build custom ERP integration (DDD, DevOps)
- scaffold Python monorepos (blog post)
- distributed cloud computing for ML (dataloader backed by ray on Azure AKS)
- native extension for Spark in Scala (github:kaiko.ai/spark-dicom)
- analysis and processing of temporal geospatial data
- speaker at PyConFr 2023: "Python moderne et fonctionnel pour des logiciels robustes" (video)
- "Arrow"-based effect system to author extendable type-safe workflows (github:tweag/funflow, blog post)
- integrate with many third party data sources
- manage ETL jobs, data freshness and data accuracy
- React: scaffold and develop
- Spring Boot: models, services, controllers, tests
- PayPal payment for an online shop
- 2019-2020: Master "Data and Artificial Intelligence", Institut Polytechnique de Paris
- 2016-2020: Ingénieur, Ecole des Ponts
I can do anything most product software companies need. https://martinfowler.com/articles/expert-generalist.html
In Data Engineering, I've done more specifically:
- analytics (Hadoop MapReduce, Spark, Modern Data Stack, superset)
- cloud data lakehouse (Spark SQL, BigQuery, Snowflake, Athena)
- parallel computing, distributed computing
- data transformation pipelines need similar features than build systems
My boring takes
- static typing is a must
- apply FP ideas (Haskell, Scala) to other languages (Python, Rust, Java)
- Inheritance is bad
- Inheritance is bad, really
- Domain Driven Design (DDD) is good
- automated testing makes you faster
- aim for 100% automated infra ops
- NixOS is ❤
- frontend: React is a good default, the Open Web Platform is most stable
- backend: most people call JSON-RPC APIs their "REST APIs", GraphQL is not a good place to start
- make a web app
- if you need native functions, make a progressive web app