Audit Data Engineering Associate: AI & Analytics

KPMG LLP; Philadelphia (Philadelphia County); On-site; USD 70,000 - 95,000; Full time

Job summary

KPMG LLP is seeking an Associate for Audit Data Engineering Services in the U.S. to join our Audit and Assurance team.

The role focuses on data acquisition, transformation, and analysis to enable high-quality audit outcomes, using tools like Alteryx, SQL, Python, and Azure-based platform services. Responsibilities include collaborating with audit teams, evaluating data landscapes, and ensuring rigorous data processing documentation.

Qualifications

• Pursuing or recently completed a Bachelor's or Master's in Information Systems, Data Analytics, Computer Science or related IT degree.
• Preferred GPA 3.0+.
• Must be eligible to work in the U.S. without sponsorship.

Responsibilities

• Data acquisition, movement, transformation and analysis to enable high-quality audit outcomes; interpret data, assess risks and deliver insights.
• Use Alteryx, SQL, Python, Azure Data Factory, Databricks, Power BI to support audit solutions.
• Communicate technical implications of D&A capabilities to audit teams and document results.
• Identify scope of D&A capabilities for audits and ensure analytic results are consistent and actionable.
• Collaborate with client tech teams to understand ERP landscape and plan data preparation and validation tasks.
• Support delivery of high-quality technology-enabled audits within a professional services culture.

Skills

Data engineering

SQL

Python

Azure Data Factory

Databricks

Power BI

Education

Information Systems / Data Analytics degree

Tools

Azure

Databricks

Power BI

Job description

KPMG LLP is seeking an Associate for Audit Data Engineering Services in the U.S. to join our Audit and Assurance team.

The role focuses on data acquisition, transformation, and analysis to enable high-quality audit outcomes, using tools like Alteryx, SQL, Python, and Azure-based platform services. Responsibilities include collaborating with audit teams, evaluating data landscapes, and ensuring rigorous data processing documentation.