Brian Lui
Senior Solutions Architect at Databricks
Canada
Summary
👤
Senior🎓
Top SchoolBrian Lui is a Solutions Architect and trusted cloud data & AI advisor with over a decade of experience helping enterprises design, implement, and operationalize modern data platforms across industries like finance, telecom, retail, and public sector. He combines hands-on implementation skills in data engineering, lakehouse architectures, governance, and BI with strategic roadmap and POC delivery, having worked at Databricks, Microsoft, Slalom, and Deloitte. A strong Azure specialist, Brian has deep experience with Databricks, Synapse, Fabric, Purview, ADF and CI/CD practices, and he accelerates adoption by balancing performance, security, cost-efficiency and operational maturity. His background includes low-level performance work on ML.NET—implementing SSE/AVX hardware intrinsics—highlighting a rare combination of systems-level optimization and enterprise data architecture expertise. Based in Canada, he is known for turning complex data challenges into practical, business-focused solutions.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Applied Science (B.A.Sc. in Eng. Sci.) Major in Engineering Science, Bachelor of Applied Science (B.A.Sc. in Eng. Sci.) Major in Engineering Science at University of Toronto
Github Skills (13)
net10
asp-net10
unit-testing10
algorithms10
machine-learning10
dotnet10
csharp10
performance-optimization10
ml10
dotnet-core10
data-structure9
algorithm9
data-structures9
Programming languages (3)
Github contributions (5)
dotnet/machinelearning
Aug 2018 - Sep 2018
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 12 PRs, 130 comments in 1 month
Contributions summary:Brian focused on optimizing the performance of ML.NET, specifically by implementing hardware intrinsics using C# and SSE/AVX instruction sets. Their work involved porting and implementing SSE and AVX support, including software fallbacks and unit/performance tests. The user also refactored code, changing call sites to utilize the `CpuMathUtils`, indicating a focus on performance improvements.
dotnetmachine-learningml
briancylui/Efficient-GAN-Anomaly-Detection
Nov 2018 - Jul 2019
Contributions:169 pushes, 1 branch in 8 months
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