Senior Internal Auditor – Data Analytics

Kraken

1d•Remote

About The Position

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.

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.

Nice To Haves

Professional certifications: CIA, CISA, CPA, CAP (Certified Analytics Professional), or cloud data certifications (AWS, GCP).
Experience building continuous auditing or continuous monitoring programs, including cloud data infrastructure such as Snowflake, AWS, or GCP.
Experience using AI/ML tools for anomaly detection, data extraction, or workflow automation in an audit or analytics context.
Familiarity with crypto, fintech, payments, or digital asset environments.

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.