Our Company:

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

About the Role

Teradata is seeking a curious, technically capable, and risk-minded Senior Operational Risk & Data Analytics Auditor to join our global Enterprise Risk and Assurance Services team. This role is designed for someone who can use data to evaluate business process performance, identify control and compliance patterns, and help ERAS deliver more scalable, analytics-enabled assurance.

This is not a traditional SOX-only or financial statement audit role. The successful candidate will support the ERAS AI and Data Analytics program, helping develop and execute data-enabled audit procedures, continuous monitoring routines, exception analysis, automation opportunities, and AI-supported assurance activities across financial, operational, compliance, procurement, people, and business process areas.

The role is well suited for someone who enjoys moving between data, process, risk, and audit evidence — someone who can analyze populations, identify meaningful risk signals, pressure-test outputs, and translate results into clear insights that management and ERAS leadership can act on.

What You’ll Do

• Support ERAS’s AI and Data Analytics area by designing, executing, and documenting analytics-enabled assurance procedures across risk-based audits, operational reviews, and management consultations.
• Analyze structured data populations to identify anomalies, trends, control exceptions, policy non-compliance, operational inefficiencies, and emerging risk indicators.
• Develop repeatable analytics routines, dashboards, exception reports, and supporting documentation that strengthen audit coverage and continuous monitoring capabilities.
• Use data analytics, process mapping, benchmarking, and root-cause analysis to evaluate operating risk, process effectiveness, governance routines, and management oversight.
• Partner with ERAS team members to advance AI-enabled audit and analytics use cases, including data preparation, logic validation, prompt/workflow design, output review, and documentation of human judgment.
• Translate analytics results into concise, management-ready insights, including what changed, why it matters, who owns the risk, and what action should be considered.
• Support testing and reporting over spend, procurement, corporate card, T&E, people, vendor, compliance, and other operational data sets as the DA program expands.
• Evaluate the design and operating effectiveness of business processes, controls, and data quality routines, including whether evidence is sufficiently complete, accurate, and defensible.
• Develop high-quality audit workpapers, analytics documentation, testing support, and reporting materials aligned with professional standards and ERAS methodology.
• Contribute to continuous improvement of ERAS analytics methodology, including standard scripts, data dictionaries, control libraries, dashboards, AI-use documentation, and review checklists.
• Bring a consultative mindset while maintaining independence, professional skepticism, data integrity discipline, and objective judgment.

What We’re Looking For

We are looking for a candidate who brings more than audit execution capability. The ideal candidate understands how organizations operate, how risks show up in data, and how analytics can help assurance teams identify issues earlier, test more intelligently, and produce better management insight.

Required Qualifications

• 5+ years of experience in internal audit, operational risk, data analytics, audit analytics, risk advisory, compliance assurance, business process reviews, or related risk/control disciplines.
• Hands-on experience using data analytics to support audits, risk assessments, monitoring routines, exception testing, or operational reviews.
• Strong understanding of operational risk, governance, internal controls, business process improvement, and evidence-based assurance.
• Ability to analyze full or large data populations and identify patterns, outliers, duplicates, policy exceptions, control gaps, or process inefficiencies.
• Practical experience with one or more analytics or reporting tools such as Excel, Power BI, Tableau, SQL, Alteryx, Python, R, or similar tools.
• Ability to validate data outputs, explain methodology clearly, and distinguish between a data anomaly, a control issue, and a broader operating risk.
• Strong written and verbal communication skills, including the ability to produce clear, concise, leadership-ready reporting based on analytics results.
• Experience working cross-functionally with business stakeholders and influencing action without direct authority.
• Bachelor’s degree in Data Analytics, Information Systems, Accounting, Finance, Business, Risk Management, Computer Science, Statistics, or a related field.

Preferred Qualifications

• Experience with audit analytics, continuous monitoring, fraud analytics, procurement/spend analytics, corporate card/T&E analytics, third-party risk analytics, people analytics, or compliance monitoring.
• Experience supporting AI-enabled workflows, automation pilots, machine-learning-adjacent analytics, prompt-based analysis, or responsible use of AI tools in audit/risk work.
• Working knowledge of data quality concepts, data lineage, data governance, model/output validation, access controls, or reporting control design.
• Experience in a global technology, SaaS, cloud, data, or software company.
• Professional certification such as CIA, CISA, CRMA, CPA, CFE, CDP, PMP, or data analytics-related credential.
• Familiarity with IIA Standards, COSO, ISO, NIST, DOJ compliance program expectations, AI governance concepts, or other governance/risk frameworks.
• Experience preparing materials for senior leadership, risk committees, audit committees, or management governance forums.

What Success Looks Like

• Expands ERAS’s ability to use data and AI-enabled tools to identify operational risk earlier and more consistently.
• Builds repeatable analytics routines that improve coverage, reduce manual effort, and strengthen audit defensibility.
• Uses data and evidence to tell a clear risk story — not just produce reports, dashboards, or exception lists.
• Validates analytics outputs with professional judgment and documents methodology clearly enough for review and re-performance.
• Partners effectively with ERAS team members and stakeholders while maintaining independence, skepticism, and objectivity.
• Produces work that is accurate, traceable, concise, and useful to management decision-making.
• Helps ERAS move from periodic point-in-time analytics toward scalable risk intelligence and continuous monitoring.

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