Senior Internal Auditor, Kraken

• 8+ years of experience in data analytics, data science, analytics engineering, or data engineering, with proven application in an audit, risk, or financial services context
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Information Systems, or a related quantitative field
• Advanced proficiency in SQL and Python. Experience building data visualizations and dashboards using tools such as Tableau, Power BI, or equivalent
• Rigorous approach to data quality, including completeness, accuracy, and reconciliation to source systems is second nature, not an afterthought
• Comfortable integrating AI-assisted tools into analytics workflows. You don't need to be an AI engineer, but you should know how to use AI to work faster and smarter
• Demonstrated ability to translate ambiguous audit or business questions into structured analytical approaches and scalable solutions
• Effective communicator who can present complex data findings as clear, actionable conclusions for auditors, senior leadership, and external stakeholders

Desirable

• This is a build role, not a traditional audit role
• You will build the data pipelines, automated tests, and monitoring tools that enable Internal Audit to test full populations and detect exceptions that sampling systematically misses
• You will also help shape how the function leverages AI — integrating AI-assisted tools into analytics workflows, building solutions that pair data analytics with emerging AI capabilities, and helping define what AI-enabled auditing looks like in practice
• You’ll be doing it at a crypto exchange — where the transaction volumes are high, the data is complex, the entities span multiple jurisdictions, and the control environment is evolving in real time
• If you want to build an audit analytics capability from the ground up where the data is genuinely interesting, this is it
• Analytics engineering & automation
• Design and build data pipelines that connect to source systems, transform raw data into audit-ready datasets, and enable full-population testing across entities and jurisdictions
• Develop automated test scripts, anomaly detection models, and risk scoring mechanisms that replace manual sampling with continuous, data-driven assurance
• Validate the completeness and accuracy of data used in analytics — you don’t just visualize data, you first prove it matches the source system
• Leverage AI-assisted tools to enhance analytics workflows, from accelerating data extraction and transformation to building smarter detection models
• Partner with SOX testers and operational auditors to translate audit objectives into scalable analytical tests
• Identify opportunities to leverage emerging technologies, including AI-assisted tools, to enhance coverage and efficiency
• Visualization & continuous monitoring
• Build and maintain dashboards and visualizations that provide Internal Audit and senior leadership with real-time insights into control effectiveness, exception trends, testing coverage, and audit findings
• Design continuous monitoring solutions that shift Internal Audit from periodic, point-in-time testing to ongoing surveillance of control health across critical processes
• Stakeholder engagement & analytics enablement
• Work with Engineering, Data, and Business Operations teams to identify data sources, understand data lineage, and secure timely access to the data needed for audit analytics
• Champion a data-first mindset within Internal Audit. Train auditors on analytics tools, build self-service datasets and documentation, and empower non-technical team members to explore data independently
• Communicate complex data analytical findings clearly to both technical and non-technical audiences, translating data into actionable audit conclusions for senior leadership and the Audit Committee

• Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis