Senior Internal Auditor focused on data pipelines and analytics at Kraken. Building automated tests and monitoring tools within a remote-first crypto exchange setting.
Responsibilities
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.
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.
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.
Requirements
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.
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