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Audit Data Analytics Jobs (NOW HIRING)

Empirical Corporation

Data Quality Analyst

Washington, DC · On-site

$95K - $105K/yr

Provide data analytics and data quality support for federal financial reporting and audit readiness activities. * Analyze data to validate completeness, accuracy, and compliance. * Develop dashboards ...

Walmart

Senior Manager, Data Analytics

Goshen, AR · On-site

$90K - $180K/yr

The Global Audit Data Analytics Team within Global Technology Audit collaborates with audit and business units to identify and mitigate risks through advanced data analytics and strategic insights.

Walmart

Senior Manager, Data Analytics

Cassville, MO · On-site

$90K - $180K/yr

The Global Audit Data Analytics Team within Global Technology Audit collaborates with audit and business units to identify and mitigate risks through advanced data analytics and strategic insights.

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Audit Data Analytics information

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$33K

$81.5K

$140K

How much do audit data analytics jobs pay per year?

As of Jun 23, 2026, the average yearly pay for audit data analytics in the United States is $81,518.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $96,500.00 per year, depending on experience, location, and employer.

How can data analytics be used in auditing?

Audit Data Analytics involves analyzing large volumes of financial and operational data to identify anomalies, trends, and risks. Auditors use tools like Excel, SQL, and specialized software to improve audit efficiency, detect fraud, and ensure compliance with regulations.

What does an audit data analyst do?

An audit data analyst examines financial and operational data to identify discrepancies, trends, and risks that could impact an organization’s compliance and accuracy. They use data analysis tools and techniques, such as Excel, SQL, or specialized audit software, to support audit processes and improve decision-making. Strong analytical skills and attention to detail are essential for this role.

How does an Audit Data Analytics professional typically collaborate with audit teams during an engagement?

Audit Data Analytics professionals work closely with auditors to identify areas where analytics can add value, such as risk assessment, transaction testing, and identifying anomalies. They often participate in planning meetings to understand the audit objectives and design data-driven procedures tailored to each engagement. Throughout the audit, they communicate findings, provide data visualizations, and help interpret analytics results, ensuring that the audit team can make informed decisions. Effective collaboration requires strong communication skills and a thorough understanding of both data analytics tools and audit standards.

Is 40 too late for data science?

For an Audit Data Analytics role, age is not a barrier to entering data science. Many professionals transition into data analytics or data science later in their careers by acquiring relevant skills such as SQL, Python, or data visualization tools, and obtaining certifications. Success depends on your skills, experience, and continuous learning rather than age.

What is Audit Data Analytics?

Audit Data Analytics refers to the use of advanced data analysis tools and techniques to enhance the audit process. By analyzing large volumes of financial and operational data, auditors can identify trends, anomalies, and risks more efficiently and accurately. This approach helps audit professionals provide deeper insights, improve audit quality, and make more informed decisions. It also increases efficiency by automating data processing and enabling continuous auditing, rather than relying solely on traditional sampling methods.

What is the difference between Audit Data Analytics vs Data Analyst?

AspectAudit Data AnalyticsData Analyst
CredentialsTypically requires accounting or audit certifications (e.g., CPA, CIA)Requires degrees in statistics, mathematics, or related fields; certifications like CAP or Microsoft certifications are common
Work EnvironmentOften within audit firms, finance departments, or internal audit teamsAcross various industries including finance, marketing, healthcare, and tech
Employer & Industry UsageUsed primarily in finance, banking, and auditing firms for compliance and risk assessmentUsed broadly in business intelligence, marketing, operations, and data-driven decision making

While both roles involve analyzing data, Audit Data Analytics focuses on auditing, compliance, and risk assessment within financial and audit contexts. Data Analysts have a broader scope, working across industries to interpret data for strategic insights. Understanding these differences helps in choosing the right career path or job focus.

Is AI replacing data analysts?

AI is transforming the role of data analysts by automating routine tasks such as data cleaning and basic analysis, allowing analysts to focus on more complex insights and strategic decision-making. While AI tools can enhance efficiency, human expertise remains essential for interpreting results, understanding context, and making nuanced judgments in audit data analytics roles.

What are the key skills and qualifications needed to thrive as an Audit Data Analytics professional, and why are they important?

To thrive as an Audit Data Analytics professional, you need strong analytical abilities, a solid understanding of auditing principles, and experience with data analysis, typically supported by a degree in accounting, finance, or information systems. Proficiency with tools such as Excel, SQL, Python, and data visualization platforms like Tableau, as well as relevant certifications like CISA or CPA, is highly valuable. Excellent problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting data and presenting findings to stakeholders. These skills and qualifications are important because they enable professionals to deliver accurate insights, enhance audit quality, and support risk-based decision-making.
More about Audit Data Analytics jobs
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What job categories do people searching Audit Data Analytics jobs look for? The top searched job categories for Audit Data Analytics jobs are:
Audit Data Analytics jobs near you
Infographic showing various Audit Data Analytics job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $81,518 per year, or $39.2 per hour.
Physical Security Systems Audit & Data Analyst

Physical Security Systems Audit & Data Analyst

Blue Star Partners

Columbus, OH • On-site

$35 - $55/hr

Contractor

Posted 3 days ago

Job description

Job Title: Physical Security Systems Audit & Data Analyst
Location: Columbus, OH (Hybrid: 3 times a week Tuesday - Thursday)
Duration: 1-Year Contract-to-Hire
Rate: $35 - $55/hr (W2)
Work Authorization: Must be authorized to work in the United States now and in the future without the need for employment-based visa sponsorship. Sponsorship is not available for this position.
Position Overview
We are seeking a Physical Security Systems Audit & Data Analyst to ensure the accuracy, integrity, and ongoing quality of physical security systems and related data across the enterprise. This role is responsible for auditing physical security platforms, validating data consistency, identifying discrepancies, and supporting long-term system reliability.
This position is ideal for someone with a strong audit, assurance, or data validation background who is highly detail-oriented and enjoys identifying anomalies, reconciling data, and improving system quality. The role is not focused on regulatory compliance, but rather on ensuring physical security systems are configured correctly, data is accurate, and records are clean and defensible.
The analyst will partner closely with Physical Security Engineering, Cybersecurity, IT, Facilities, and vendors to support a mature and well-governed physical security environment.
Technologies Utilized
  • SiteOwl
  • CCure
  • Avigilon
  • SureView
  • Microsoft Excel
  • Microsoft Office Suite (Word, Outlook, PowerPoint)

Required Qualifications
  • 4+ years of experience in audit, assurance, data validation, risk analysis, or control testing
  • Strong analytical skills with experience reviewing large and complex datasets
  • Demonstrated ability to identify inconsistencies, anomalies, and data quality issues
  • Exceptional attention to detail and accuracy
  • Experience working in structured, process-driven environments
  • Ability to manage multiple review efforts simultaneously without sacrificing quality
  • Strong documentation skills with the ability to clearly record findings, recommendations, and outcomes
  • Advanced proficiency with Microsoft Excel and strong working knowledge of Word, Outlook, and PowerPoint
  • Strong problem-solving and troubleshooting skills
  • Clear and effective written and verbal communication skills

Preferred Qualifications
  • Background in corporate tax audit, internal audit, financial audit, or assurance functions
  • Experience performing reconciliations between systems, applications, or databases
  • Familiarity with physical security systems, access control concepts, or cybersecurity fundamentals
  • Experience working with technologies such as SiteOwl, CCure, Avigilon, or SureView
  • Exposure to IT systems, identity/access management, or operational monitoring environments

Key Responsibilities
System Audits & Data Validation
  • Perform detailed audits of physical security systems including access control, video management, and alarm platforms
  • Validate data accuracy and completeness across multiple physical security tools and systems of record
  • Reconcile users, permissions, devices, alarms, and configuration data between upstream and downstream systems
  • Identify discrepancies, misconfigurations, orphaned records, outdated entries, and systemic data issues

Data Integrity & Governance
  • Ensure physical security systems remain clean, consistent, and trustworthy through recurring review and validation
  • Verify that system configurations and data align with established internal standards and approved designs
  • Track and document findings, corrections, and validation outcomes to support long-term system reliability

Issue Analysis & Remediation Support
  • Analyze root causes of data inconsistencies and recurring errors
  • Work with Physical Security Engineering, Cybersecurity, IT, Facilities, and vendors to resolve identified issues
  • Confirm corrective actions are accurately implemented and sustained over time

Process & Quality Improvement
  • Identify patterns, trends, and systemic weaknesses in system data or configurations
  • Support improvements to processes that reduce manual error, improve repeatability, and enhance auditability
  • Help develop structured review routines, validation checklists, and standard operating procedures

Reporting & Communication
  • Prepare clear, concise summaries of findings, discrepancies, trends, and corrective actions for internal teams and leadership
  • Translate technical system information into understandable observations and recommendations
  • Communicate issues in a factual, evidence-based manner with clarity and precision

Learning & Technical Development
  • Develop a working knowledge of physical security technologies, system architecture, and data flows through training and hands-on experience
  • Learn how physical security tools integrate with cybersecurity platforms, identity systems, and operational monitoring processes

Key Attributes for Success
  • Meticulous, methodical, and highly detail-oriented
  • Comfortable questioning data, validating assumptions, and investigating inconsistencies
  • Strong sense of ownership for data quality and system accuracy
  • Organized, disciplined, and process-driven
  • Able to work independently while collaborating effectively with technical and business teams
  • Strong curiosity and willingness to learn new technologies and systems

Success in This Role
  • Physical security systems and records remain accurate, clean, and reliable
  • Data discrepancies and configuration issues are identified and resolved proactively
  • Internal teams have greater confidence in the integrity of system data and reporting
  • Processes become more repeatable, auditable, and less prone to error

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