Full Stack Engineer- Internal Audit

Details

• Build the Future of Assurance: Develop end-to-end (full stack) applications that automate control testing, and risk monitoring across Cloudflare’s global infrastructure.
• Data-driven Audit Execution: Provide hands-on audit support by leveraging data science and advanced analytics to execute data-driven testing throughout the audit lifecycle.Customer Zero Engineering: Build and maintain internal tools and dashboards using Cloudflare’s stack (e.g., Workers, KV, D1, AI Gateway) to solve complex internal risk and compliance challenges.
• AI-Driven Risk Detection: Integrate LLMs and machine learning pipelines to analyze vast datasets for anomalies, potential security misconfigurations, or compliance deviations.
• Engineer for Scale: Move beyond manual scripting. Build high availability microservices that pull data from APIs, databases, and production logs to deliver near real-time risk insights.
• Data Strategy: Partner with Data Engineers and data owners to ensure we have clean, actionable telemetry for internal audits and continuous auditing.
• Own the Stack: Take full ownership of the lifecycle - from database schema design and API development to UI implementation and production deployment.
• Bachelor’s or master’s degree in computer science, Software Engineering, or equivalent experience.
• 5+ years of full stack software engineering experience with proven industry experience in a large-scale environment (high-throughput, distributed systems & globally distributed teams).
• A genuine interest in technology risk management, internal audit methodologies, or engineering compliance frameworks.
• Strong experience in application development using backend languages like Python, Go, or Node.js and modern frontend frameworks like React or TypeScript.
• Deep understanding of SQL/NoSQL databases, API design (REST/GraphQL), and distributed systems.
• Hands-on experience with cloud-native architectures. Experience with Cloudflare Workers, AWS, or GCP is highly preferred.
• Familiarity with data engineering pipelines (ETL/ELT) and leveraging LLMs/ML for automated data analysis.
• Familiarity with technology risk management, internal audit methodologies, or engineering compliance frameworks is preferred.
• Ability to explain complex technical risks to non-technical stakeholders and translate audit requirements into elegant code