Marcos Salles
Senior Software Engineer at Datadog
Lisbon Metropolitan Area Portugal
Summary
🤩
Rockstar🎓
Top SchoolMarcos Salles is a senior software engineer and researcher with 11 years of experience building low-latency, data-intensive systems, currently driving observability platform work at Datadog. He combines a strong academic pedigree—a PhD from ETH Zurich and faculty roles at the University of Copenhagen—with hands-on Rust-based development on the Materialize streaming SQL engine, where he improved core time-series and interval handling. His work spans transactional and streaming databases, in-memory and spatial systems, and actor-oriented architectures, often focused on reducing query latency and memory while improving reliability. He has contributed to widely used open-source dataflow components and brings uncommon breadth from academic research to production-grade cloud-native systems. Based in Lisbon, he also has a history of architecting applied systems for industry clients, from helicopter logistics to large-scale monitoring tools.
11 years of coding experience
20 years of employment as a software developer
Pontifical Catholic University of Rio de JaneiroPhD Computer Science, PhD Computer Science at ETH ZürichBSc Computer Science, BSc Computer Science at Universidade Estadual de Campinas
English, Portuguese
Github Skills (11)
time-series10
rust10
postgresql10
sql10
data-structure9
streaming-data9
stream-processing9
distributed-systems9
data-structures9
databases8
database8
Programming languages (1)
Github contributions (5)
MaterializeInc/materialize
Jul 2022 - Jan 2023
Real-time Data Integration and Transformation: use SQL to transform, deliver, and act on fast-changing data.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:535 reviews, 24 commits, 159 PRs in 6 months
Contributions summary:Marcos contributed to the Materialize project by implementing and modifying core back-end components related to time series data, interval calculations, and dataflow processing. They focused on improving the SQL INTERVAL datatype, increasing its range to match PostgreSQL, and resolving issues in the time string parsing logic. The contributions involved modifications to the `datetime.rs`, `interval.rs`, and `reduce.rs` files, reflecting a strong understanding of the project's core data processing and storage functionalities. The user also improved debugging messages.
streamsdata-warehousedistributed-sql-databasestreamingpostgresql-dialect
vmarcos/materialize
Jul 2022 - Jan 2024
The Fastest Way to Build the Fastest Data Products. Build data-intensive applications and services in SQL — without pipelines or caches — using materialized views that are always up-to-date.
Contributions:1 review, 1 PR, 890 pushes in 1 year 6 months
materialized-viewsdatawarehousemysqlsqlfastest
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.