Akash Srivastava
Lead Software Engineer, Careem (Uber)
Dubai, United Arab Emirates
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Lead Software Engineer with 10+ years of experience specializing in designing and implementing distributed systems, Data, ML & AI platforms processing batch & streaming data @ 9 Million EPS (18+ PB/day). Proficient in Java, Scala, Python, and cloud platforms (AWS, Azure, GCP), with proven success in leading large-scale data processing projects handling petabytes of data and building services serving millions of requests per second.Adept at driving efficiency and scalability through innovative solutions, including patented technologies.
I am currently leading the Data Platform team at Careem (Uber).
Area of Expertise
Topics
From Prompt to PR: Sandbox-Validated dbt Changes Before Human Review”
A 5-member DWH team. 100+ concurrent requests. 5,000+ active ETLs. We tried the shortcut: let Claude generate SQL and have engineers review it. It failed: code generation without validation just shifts the burden; engineers still had to mentally execute every model. The real answer: full-stack sandbox validation. From Prompt to PR, every AI-generated model clears four layers before a human sees it: SQL compilation, dbt tests (uniqueness, range, relationships), orchestration via auto-generated Airflow DAG, and lineage integrity in OpenMetadata. All four must pass. Failures loop back to the agent, not the engineer. Guardrails are structural: read-only prod ServiceAccount, macro-enforced dev schema isolation, ephemeral Kyuubi compute per run. In production: 45% of new models carry the "ai-generated" tag. You'll leave with: the full-stack sandbox architecture, the four-layer validation blueprint, structural guardrails, and governed promotion via explicit PR reviews.
dbt Summit 2026 Sessionize Event
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