cover_letter / art_r-u_oSTI3Ug

Dear MongoDB Internal Data Hiring Team, MongoDB has built the database infrastructure that powers some of the most consequential software on the planet — and the internal data team that keeps MongoDB itself running with the same rigor is exactly the kind of platform-first, product-minded environment where I do my best work. My interest in this role is grounded in a specific experience: at Intuit, I owned the ICE platform from the inside out — building self-service infrastructure, working directly with telemetry and usage data in BigQuery and SQL, and translating messy, cross-functional requirements into clear product decisions that scaled to 675M+ engagements in FY23. That experience taught me that internal data products are only as good as the trust the people who depend on them place in them — and that trust is earned through rigor, communication, and relentless focus on user outcomes. **Technical and Data Foundation** My technical foundation spans the full stack of what this role requires. At Intuit, I worked closely with SQL and BigQuery to surface developer pain points across ~20 mobile apps and 30+ product SKUs, and I built Asterias — a declarative asset lifecycle management platform with a GraphQL API — to give engineering teams structured visibility into asset state across the organization. I designed and shipped the ICE Self-Service platform (DevPortal, GitOps config, ICE Playground), reducing developer onboarding from 2–3 weeks to minutes in pre-production. That work required me to hold both the user experience and the underlying data architecture in mind simultaneously — exactly the balance this Finance data product role demands. On the data infrastructure side, I have hands-on experience with pipeline design, API development, and platform scalability. At Intuit, I led a GCP-to-AWS migration for Mailchimp's MSaaS infrastructure and drove a throughput scaling initiative from 6K to 50K TPS via rSocket migration — work that required deep collaboration with engineering on technical tradeoffs while keeping business outcomes front and center. I also initiated and led a Drift Detection and Resolution program, writing a Java JAR library to scan Git repositories for configuration drift and building a remediation roadmap using OpenRewrite — a project that mirrors the kind of data quality and governance work that Finance data products require. Beyond my Intuit tenure, I have built production data pipelines independently. My Fintellect AI platform includes a RAG retrieval pipeline with a ChromaDB vector store, multi-provider LLM orchestration with fallback routing, structured output validation, and token budget optimization. My aeval evaluation platform uses a FastAPI orchestrator, TimescaleDB for time-series metric storage, and a Redis job queue — with statistical rigor built in (bootstrap confidence intervals, Welch's t-test, Cohen's d effect size). These projects reflect a consistent pattern: I build data systems with production-grade architecture, not just prototypes. **Why This Role** The Finance domain at MongoDB — planning, forecasting, spend efficiency, financial performance analysis — is where data products have the highest stakes and the least tolerance for ambiguity. My background bridges the technical depth to work credibly with engineering on pipeline and API design, and the business and financial training (MBA with concentrations in Finance, Quantitative Analysis, and Economics from Carnegie Mellon's Tepper School) to speak the language of the Finance stakeholders I would be serving. That combination is not common, and I believe it is exactly what this role requires. **Role-Specific Connection** What excites me most about this role is the explicit commitment to treating data as a product — applying the same user focus and outcome orientation to internal data that MongoDB brings to Atlas. I have lived that philosophy at Intuit, where I owned backlogs, wrote PRDs and user stories, ran sprint planning, and drove adoption through documentation and onboarding. The Finance domain's emphasis on planning and forecasting workflows also connects directly to my quantitative background: I built a Monte Carlo simulation model at Bank of America Merrill Lynch to optimize phase distribution across a $494M portfolio, which gave me early exposure to the kind of structured financial analysis that Finance teams at a company like MongoDB depend on daily. **Selected Relevant Experience** - **Intuit — ICE Self-Service Platform:** Delivered DevPortal, GitOps config, and ICE Playground, reducing developer onboarding from 2–3 weeks to minutes in pre-prod and under 24 hours for production; mitigated $1M+ in projected opex growth through self-service adoption. - **Intuit — Data-Driven Prioritization:** Used SQL and BigQuery telemetry to prioritize developer pain points across ~20 mobile apps and 30+ product SKUs; built Asterias, a declarative asset lifecycle management platform with GraphQL API. - **Intuit — Scale and Throughput:** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; scaled throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99. - **Intuit — Enterprise Language Assessment:** Conducted enterprise-wide Service Language Assessment across 9 languages, analyzing usage data and developer feedback to inform strategic investment decisions presented to the CTO. - **Splunk — Search Platform Ownership:** Owned Search Service (Go microservices), Search Catalog (PostgreSQL metadata service), and SPL/SPL2; delivered Scheduler Service end-to-end in ~4 months and achieved up to 10x query performance improvements for a Fortune 500 beta customer. - **Fintellect AI — Data Pipeline Architecture:** Architected RAG retrieval pipeline with ChromaDB vector store, multi-provider LLM orchestration with fallback routing, structured output validation, and token budget optimization — production-grade data infrastructure built independently. - **Bank of America Merrill Lynch — Financial Modeling:** Developed enhanced estimation model using Monte Carlo simulation to optimize DMAIC phase distribution across a $494M portfolio in Global Technology & Operations. **Closing** MongoDB's mission — enabling organizations to modernize, innovate, and build the AI-era applications that matter — depends on the internal data infrastructure being as reliable and well-designed as the platform itself. I want to be part of the team that holds that standard for the Finance domain: building pipelines, datasets, and dashboards that Finance teams trust completely, and that give MongoDB the financial visibility it needs to keep growing at scale. I would welcome the opportunity to discuss how my background maps to what you are building. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info