Internal Audit Senior Analyst, Data Analytics

Salesforce•Indianapolis, IN

2d•Hybrid

About The Position

Salesforce is seeking an Internal Audit Senior Analyst to design and deliver AI and analytics solutions that support Internal Audit engagements from start to finish. This includes scoping, data extraction, analysis, visualization, and reporting. The role involves working directly with audit teams throughout the audit lifecycle (risk assessment, planning, execution, and reporting). Responsibilities include building AI logic, SQL queries, Python scripts, and Tableau dashboards to enhance the audit process, making it more defensible. The position also requires some administration of the team's audit tooling.

Requirements

2–4 years of experience in AI, Agentic AI, data analytics, audit analytics, business intelligence, or a related analytical role. (any or all of the following: Claude/Cursor, Tableau, Snowflake, Python)
1–2 years of experience in audit, internal audit, risk security or a related field
Experience building and deploying production-grade LLM systems, including agents, tool integration, MCP, retrieval/grounding, and prompt optimization. You know what it takes to monitor and scale real AI workflows.
Strong principles around agent engineering, from tool scoping and grounded reasoning to safe failure handling and continuous model evaluation.
Strong proficiency in SQL for querying and manipulating large, complex datasets.
Working proficiency in Python for data analysis and scripting.
Capability to use probabilistic models and tune to behave as deterministically as possible for audit-defensible results.
Experience building dashboards or reports in Tableau (or a comparable BI tool).
Demonstrated ability to learn business systems and processes quickly, gather business requirements directly from stakeholders, and reason about data flow, controls, and risk.
A proactive, self-starting approach to identifying and solving problems, not just executing assigned tasks.
Comfort with basic system administration tasks (user access, configuration) on business platforms such as an eGRC tool.
Strong written and verbal communication skills, including the ability to train and enable non-technical colleagues on new tools and workflows.
Bachelor's degree in a quantitative, business, accounting, or related field, or equivalent practical experience.

Nice To Haves

Prior experience in internal audit, external audit, risk management, or compliance analytics.
Hands-on administration experience with an GRC platform
Familiarity with control frameworks (e.g., COSO, SOX).
Experience with additional analytics or automation tools.
Exposure to Salesforce's own platform and data model.

Responsibilities

Partner with Internal Audit teams throughout the audit lifecycle, building and applying the right solutions and capabilities.
Develop optimized SQL queries and AI system prompts (.md) to seamlessly extract, profile, and reconcile high-volume data from financial, operational, and source platforms.
Build Python and other scripts for data cleaning, transformation, and repeatable audit testing (e.g., exception testing, continuous assurance/monitoring).
Use AI tools and other capabilities to speed up data analysis, summarize findings, draft work papers, and build lightweight internal tooling.
Design clear, audit-ready visualizations and dashboards in Tableau that communicate risk and findings to both technical and non-technical stakeholders.
Develop a working understanding of the business systems in scope (e.g., ERP, CRM, ticketing, HR, or financial systems) well enough to assess data lineage, controls, and where risk actually lives.
Provide light system administration support for the team's GRC and other tooling, including user access, workflow/queue configuration, and basic troubleshooting.
Proactively identify AI and analytics opportunities and process gaps rather than waiting to be asked, flag risk trends, data quality issues, and automation opportunities as you see them.
Support training and enablement for audit staff on analytics tools, dashboards, and AI-assisted workflows so the broader team can self-serve.
Document methodology, assumptions, and results so analytics can be re-run, reviewed, and defended by audit leadership and external reviewers.