Sai Annepu
Vice President at Goldman Sachs
Bengaluru, Karnataka, India
Summary
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Rockstar🎓
Top SchoolSai Sriharsha Annepu is a Vice President in Goldman Sachs’ Data Engineering unit with nine years of progressive experience across analyst to leadership roles, focused on building reliable, production-grade data systems. An IIT Kharagpur computer science graduate with near-perfect academics, he blends deep technical craft with team leadership to deliver scalable backend solutions. He contributes to open-source through meaningful backend work on the finos/legend-engine project, improving execution planning, error reporting, and graph-fetch capabilities—underscoring expertise in query execution and engine internals. Based in Bengaluru, he brings a pragmatic, engineering-first approach to complex data challenges and a track record of shipping features that bridge research-grade ideas and real-world trading-scale systems.
5 years of coding experience
3 years of employment as a software developer
High School - Intemediate, 98.3%, High School - Intemediate, 98.3% at Sri Chaitanya College of EducationHigh School, High School/Secondary Certificate Programs, 10GPA, High School, High School/Secondary Certificate Programs, 10GPA at Visakha Valley SchoolB.Tech (Hons.), Computer Science and Engineering, 9.78/10.00, B.Tech (Hons.), Computer Science and Engineering, 9.78/10.00 at Indian Institute of Technology, Kharagpur
English, Telugu, Hindi
Stackoverflow
82reputation
2kreached
14answers
3questions
Github Skills (13)
mappings10
javas10
java10
sql9
api8
apidoc8
html6
javascript6
validation6
forloop6
loops6
regex6
compiler-construction6
Programming languages (4)
Github contributions (5)
finos/legend-engine
Jan 2021 - Jan 2023
Legend Engine module
Role in this project:
Back-end Developer
Back-end DeveloperContributions:421 reviews, 89 commits, 493 PRs in 2 years
Contributions summary:Sai primarily worked on enhancing the Legend Engine module, as evidenced by the numerous commits focused on modifying core execution plan components. Their contributions include implementing features within the in-memory execution node executor, enhancing error reporting, and improving query parsing. The user also added support for new features, such as local properties and cross-property mappings, further demonstrating their involvement in extending the engine's capabilities. Furthermore, the user participated in the integration of graph fetch features.
modelinglegend
finos/pylegend
Feb 2023 - Mar 2023
pylegend
Contributions:4 releases, 1 review, 40 commits in 21 days
legendmodeling
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