cover_letter / art_pjNBFmLOPI4

Dear Snowflake Migrations Hiring Team, Snowflake is building the infrastructure layer for the agentic enterprise — and the Migrations team sits at the most consequential bottleneck in that mission: the years-long, million-dollar untangling of legacy data systems that stands between enterprises and the AI Data Cloud. That problem is precisely the kind I have spent 12+ years learning to solve — first as a Staff PM at Intuit scaling developer platform infrastructure to 675M+ engagements, and now as a founder building GenAI-powered products from the ground up. When I read that your team believes Generative AI is the key to turning multi-year migration roadblocks into streamlined, intelligent, automated experiences, I recognized both the problem framing and the conviction behind it. --- **Technical Foundation** My technical credibility in the data and AI space is grounded in hands-on work across the full stack — not just product oversight. At Intuit, I owned the ICE platform's developer infrastructure across ~20 mobile apps and 30+ product SKUs, working directly with SQL and BigQuery telemetry to prioritize engineering investment, and leading a Mailchimp GCP-to-AWS migration that required delivering a Golang service template, MySQL persistence integration, and updated DevPortal documentation under a hard production deadline. I also initiated the MSaaS Drift Detection and Resolution program — writing a Java JAR library to scan Git repositories for configuration drift and building a remediation roadmap using OpenRewrite. These are not PM-adjacent activities; they are the kind of technical depth the Migrations role demands when balancing ETL pipeline constraints, data type compatibility, and post-migration validation against engineering feasibility. On the AI side, I built aeval — a local-first model evaluation platform with FastAPI orchestration, TimescaleDB, Redis job queuing, and a Next.js dashboard — incorporating bootstrap confidence intervals, Welch's t-test, Cohen's d effect size, and automated safety gates for CI/CD regression detection. I built an RL post-training workbench benchmarking GRPO, DPO, PPO, and nine other algorithms across TRL, VeRL, OpenRLHF, and NeMo RL with live SSE metric streaming. And at Fintellect AI, I architected a RAG retrieval pipeline with ChromaDB, multi-provider LLM orchestration across Claude, GPT-4, and Gemini with fallback routing, and structured output validation — directly analogous to the semantic validation and code translation challenges your GenAI-powered migration engine faces. My NeurIPS 2014 publication on neural networks for protein structure prediction, and the 2026 rewrite of that system spanning 413 parameters to 8B, reflect a research-to-production arc that informs how I think about AI feature design and evaluation rigor. --- **Why This Role** The through-line of my career — from SOA platform PM at Kaiser Permanente, to Search Orchestration at Splunk, to Developer Frameworks at Intuit, to founding AI-native products — is building developer-facing infrastructure where the user experience of complexity is the product. The Migrations team is doing exactly that at Snowflake scale: abstracting the complexity of ETL pipelines, SQL dialect translation, and data application logic rewriting behind an intelligent, automated surface. That is a product challenge I am built for. What specifically excites me about this role is the combination of GenAI-powered code and logic translation with the GTM complexity of Professional Services and GSI partner ecosystems. I have operated at that intersection — at Intuit, I delivered the ICE Self-Service platform (DevPortal, GitOps config, ICE Playground) that reduced developer onboarding from 2–3 weeks to minutes, while coordinating with design, engineering, and partner teams to drive adoption. I understand that a migration feature is only as valuable as its enablement pathway, and I know how to build both. --- **Selected Relevant Experience** - **Intuit — GCP-to-AWS Migration (Mailchimp MSaaS):** Delivered Golang service template, MySQL persistence integration, and DevPortal documentation updates to meet a hard production deadline — direct experience with the technical and coordination demands of platform migrations. - **Intuit — ICE Self-Service Platform:** Reduced developer onboarding from 2–3 weeks to minutes in pre-prod and under 24 hours for production; mitigated $1M+ in projected opex growth. Scaled ICE engagements 275% YoY to 675M+ in FY23. - **Intuit — MSaaS Drift Detection & Resolution:** Wrote Java JAR library to scan Git repos for configuration drift; built remediation roadmap using OpenRewrite — directly applicable to post-migration validation and configuration consistency challenges. - **Intuit — Enterprise Service Language Assessment:** Conducted analysis across 9 languages (Java, Python, Kotlin, Go, TypeScript, Scala, PHP, C++, Groovy) using usage data and developer feedback to inform CTO-level strategic decisions — the kind of structured, data-driven analysis required to prioritize migration feature investment. - **Splunk — Search Orchestration PM:** Owned Go microservices (Search Service), PostgreSQL metadata service (Search Catalog), and SPL/SPL2 query language product; delivered Scheduler Service end-to-end in ~4 months and achieved up to 10x query performance improvements for a beta Fortune 500 customer. - **Fintellect AI — RAG Pipeline & LLM Orchestration:** Architected multi-provider LLM orchestration (Claude, GPT-4, Gemini) with fallback routing, structured output validation, and token budget optimization — practical GenAI delivery experience directly relevant to building a migration co-pilot or semantic validation layer. - **aeval — AI Evaluation Platform:** Built statistical evaluation infrastructure (bootstrap CIs, Welch's t-test, Cohen's d, saturation detection) with CI/CD regression gates — the evaluation rigor needed to validate GenAI-powered SQL translation and logic rewriting at scale. --- **Closing** Snowflake's mission — to power the agentic enterprise through an AI Data Cloud that every organization can trust — depends on solving the migration problem cleanly. Enterprises cannot build on a modern data foundation while their legacy ETL pipelines and data applications remain untouched. The Migrations team is removing that barrier, and I want to help build the product that makes it possible. I bring the technical depth to engage meaningfully with engineering on data type constraints and pipeline feasibility, the platform PM track record to drive adoption through developer tooling and partner ecosystems, and the hands-on GenAI experience to contribute to the intelligent core of what you are building. I would welcome the opportunity to discuss how my background maps to your current roadmap priorities. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info