Senior Level Health Data Analyst Jobs

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.

Most Solutions Architect roles at Amazon are customer-facing and advisory. This one is internal-facing and hands-on. Your customers are Internal Audit's audit teams — the people who carry testing to the business and assure risk coverage across the enterprise. You sit within the Data Analytics team, report to the Head of Internal Audit Data Analytics, and work as an integration partner across the sub-teams rather than belonging to any one of them. The goal isn't just a faster pipeline. It's a platform audit teams can actually use without building workarounds for every engagement.

The team is also investing in agentic technology to move Internal Audit from reactive to proactive testing. The target state is agentic pipelines that surface risk and generate test procedures before teams even open an engagement. Applying LLM-driven orchestration to audit testing at this scale hasn't been done before on this team, so you'll be defining the patterns rather than following an existing playbook — and building expertise that few organizations have yet.

This is a highly visible technical individual contributor role that spans Internal Audit's full domain. If you're comfortable operating where the mission is clear but the technical path isn't, we'd like to hear from you.

Key job responsibilities

• Own the integration architecture across BI/Analytics, Applied Science, and Data Engineering sub-teams, designing the pipeline that connects analytical tooling, risk models, and data infrastructure into a unified testing workflow.
• Write SQL, build ETL/ELT processes, create data models, and implement agentic workflows using LLM orchestration and tool-use chains with human-in-the-loop validation to move audit testing from reactive to proactive.
• Investigate architectural constraints and competing priorities across sub-teams, drive alignment on data models and pipeline patterns, and resolve technical disagreements through hands-on analysis rather than positional authority.
• Author 6-pagers, technical design documents, and reference architectures; present architecture decisions and trade-offs to Director and VP-level audiences within Internal Audit leadership.
• Define reusable patterns for agentic audit pipelines that surface risk, generate test procedures, and speed up engagement execution across the enterprise.

A day in the life
You might start by reviewing a data pipeline prototype with the Data Engineering sub-team, working through schema decisions that affect downstream risk models. By midday, you could be presenting an integration architecture proposal to Internal Audit leadership, walking through trade-offs and fielding technical questions. In the afternoon, you may pair with the Applied Science team to wire an LLM-driven orchestration step into the testing workflow, then close the day drafting a technical design document that captures decisions made and open items for the next sprint.

About the team
The Data Analytics team sits within Amazon's Internal Audit organization and provides the analytical tooling that supports audit coverage across AWS, Stores and Delivery Operations, and Subsidiaries. Three sub-teams (BI/Analytics, Applied Science, and Data Engineering) each bring specialized capabilities, and this role exists to integrate them into a single testing pipeline. We report to the Head of Internal Audit Data Analytics and are investing in agentic technology to make audit testing proactive rather than reactive. If you want to define new patterns at the intersection of data engineering, applied science, and LLM-driven automation, this is the team to do it.