Solutions Architect III, Internal Audit Data Analytics

Amazon's Internal Audit organization covers all of AWS, Stores & Delivery Operations, and Subsidiaries. The Data Analytics team is responsible for the analytical tooling that makes that coverage possible — and right now, that tooling is spread across several sub-teams, each operating their own dashboards, risk models, and data pipelines. These tools work well independently, but they haven't been integrated into a single testing pipeline, which means audit teams still manually piece together outputs from different systems before they can run a test.

You will own the integration strategy that brings these pieces together. The work starts with understanding what each team has already built and why they built it that way. Dashboards don't feed into risk models. Data pipelines don't inform test generation. Science outputs sit in notebooks instead of running in production. You'll evaluate which AWS services and internal builds fit each integration point, write the technical design for how they connect, and then work across teams to implement it. You don't hand off implementation — you make the technical calls and you write the code.

That also means navigating disagreement. Several teams built their tools for different purposes under different architectural constraints, and they won't always agree on data models, pipeline patterns, or what to prioritize. Before you propose changes, you need to understand the real constraints behind each team's decisions. Your credibility comes from what you build, not your title.

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