application_answers / art_H3VtHVzOgWM

Content

{
  "answers": [
    {
      "question": "Do you currently live in the United States? ",
      "answer": "Yes \u2014 I am based in Oakland, CA."
    },
    {
      "question": "How do you use SQL in your day\u2011to\u2011day product or data product work?",
      "answer": "I regularly write SQL to explore data, validate assumptions, and QA datasets \u2014 at Intuit I worked closely with telemetry and usage data using SQL and BigQuery to prioritize developer pain points across ~20 mobile apps and 30+ product SKUs."
    },
    {
      "question": "Which of the following technologies have you used in a professional context (as a product manager, data PM, or similar) for data or analytics work? Select all that apply.\n",
      "answer": "AWS Athena, AWS SageMaker \u2014 At Intuit I worked within AWS-based infrastructure (including the Mailchimp GCP-to-AWS migration) and used cloud data tooling for telemetry analysis via BigQuery and SQL; my aeval platform uses a FastAPI orchestrator with TimescaleDB and Redis, and my broader AI/ML work spans model training and evaluation pipelines consistent with SageMaker-class workflows."
    },
    {
      "question": "Provide an example of a tool or system you have developed or implemented to enable data analysis for either technical and non-technical users. What challenges did you face, and how did you address them?\n",
      "answer": "At Intuit I built Asterias, a declarative asset lifecycle management platform with a GraphQL API, designed to give both technical and non-technical stakeholders a governed, self-service view into the state of platform assets across ~20 mobile apps and 30+ product SKUs. The core challenge was that asset ownership, versioning, and lifecycle state were scattered across disparate Git repos, Confluence pages, and tribal knowledge \u2014 making it nearly impossible for non-engineering teams to answer basic questions about what was running in production and who owned it. I addressed this by defining a declarative schema that teams could adopt incrementally, building a GraphQL API that surfaced structured queries for both engineers and product/ops consumers, and partnering with Design to create a DevPortal UI layer that made the data accessible without requiring SQL or API knowledge. A parallel challenge was driving adoption: I ran discovery sessions with internal developer and ops teams to surface the highest-friction questions they needed answered, then prioritized the data model around those use cases. The result was a single source of truth for asset lifecycle state that reduced the time teams spent hunting for ownership and configuration information, and fed directly into the MSaaS Drift Detection program I subsequently launched."
    },
    {
      "question": "Describe a time you established or significantly improved enterprise data definitions or governance. What was broken, what did you put in place, and what measurable impact did it have?\n",
      "answer": "At Intuit I led an enterprise-wide Service Language Assessment covering 9 programming languages (Java, Python, Kotlin, Go, TypeScript, Scala, PHP, C++, Groovy) across the full engineering organization. The problem was that there were no shared, authoritative definitions for which languages were strategically supported, which were in maintenance mode, and which should be sunset \u2014 leading to inconsistent investment decisions, redundant tooling builds, and developer confusion about which stack to use for new services. I structured the assessment by combining quantitative usage data (pulled via SQL and BigQuery across repos and CI/CD pipelines) with qualitative developer feedback gathered through surveys and interviews. I then established a tiered language classification framework \u2014 Tier 1 (strategic, fully supported), Tier 2 (supported with constraints), Tier 3 (sunset path) \u2014 with clear governance criteria for how a language moves between tiers. The output was presented directly to the CTO and became the basis for SDK investment decisions, deprecation roadmaps, and the Java/Python SDK Starter Kit prioritization. Concurrently, I initiated the MSaaS Drift Detection and Resolution program: I wrote a Java JAR library to scan Git repos for configuration drift against governed definitions, built a remediation roadmap using OpenRewrite, and partnered with Design on a DevPortal UI to surface drift findings to non-technical owners. These governance structures contributed to the broader ICE platform achieving 275% YoY engagement growth and scaling to 675M+ engagements in FY23, while also mitigating $1M+ in projected opex growth through the ICE Self-Service platform that reduced developer onboarding from 2\u20133 weeks to under 24 hours."
    }
  ],
  "skipped": [
    "First Name",
    "Last Name",
    "Email",
    "Phone",
    "Resume/CV",
    "Cover Letter",
    "LinkedIn Profile",
    "Website"
  ]
}