Data Analyst (SOX/Internal Audit), Business Operations

IPE testing (SOX) – test completeness and accuracy of Information Produced by Entity relied on as audit evidence, across ITGC, ITAC, and process control testing. Data sourcing – work with IT and process owners to extract the right data from a complex, ~50-system landscape (off-the-shelf, vendor-unsupported legacy, and in-house backend systems), including pulling targeted data out of pooled/monolithic structures like our older Oracle ERP. Data preparation – examine, clean, structure, and validate extracted data for completeness and accuracy before it's used in IA testing. Analytics – use Power BI, pivot tables, and similar tools to surface trends and anomalies for internal auditors to follow up via detailed testing. AI-enabled tooling – build practical tools for IA and SOX (e.g., testing automation, issue/action tracking, anomaly triage). AI audit readiness – support the Head of Internal Audit in developing IA's approach to auditing emerging AI-driven processes (e.g., AI agents); a contributing, research-oriented strand rather than a standalone workstream in year one.

2–4 years in data analytics, audit analytics, or a similar analytical role – audit, risk, or controls environment preferred. Strong hands-on skills: SQL, Power BI (or Tableau), Excel (pivot tables, Power Query); scripting (e.g., Python) for data cleaning and analysis a strong plus. Comfortable extracting data from legacy/complex ERP environments (pooled or poorly-segmented data structures), not just clean, well-documented systems – and patient enough to work iteratively with IT/system owners to get there. Exposure to IPE / system-generated report reliability testing a plus – coachable if not already familiar with SOX terminology. Curiosity and some practical exposure to AI tools/APIs (e.g., building automations or simple AI-assisted tools) – this is an emerging part of the role, not a full-time research function. Comfortable working across multiple teams and stakeholders (both Senior SOX Auditors, process owners, IT); self-starter, strong data instincts, and clear communication translating data into findings auditors can act on.