Payroll Data Integrity & Controls Analyst

PepsiCo Inc.; San Nicolás de los Garza; On-site; MXN 420,000 - 610,000; Full time

Job summary

PepsiCo Inc. is seeking an Employee Data & Payroll Controls Analyst in the Monterrey area. You will ensure accuracy and synchronization of employee and compensation data across payroll and HR systems, supporting post-payroll controls, data reconciliations, and reporting initiatives to strengthen controls.

You will collaborate with cross-functional teams, analyze high-impact payroll cases, and drive actions to improve data quality and process effectiveness.

Qualifications

• Bachelor's degree required or equivalent in a related field.
• 2-4 years of experience in Payroll, HR Operations, Workforce Administration, Data Analysis, or related fields.
• Strong analytical and problem-solving skills; advanced Excel.
• Advanced English language skills; excellent communication.

Responsibilities

• Reconcile critical employee and compensation data across payroll & HR systems.
• Run post-payroll controls to identify discrepancies.
• Investigate data mismatches and coordinate corrections with stakeholders.
• Validate employee changes, compensation updates, and payroll-related transactions.
• Support data review activities for sector initiatives and process improvements.
• Collaborate with cross-functional teams to ensure data integrity.

Skills

Advanced Excel

Advanced English

Education

Bachelor's degree in Business Administration, Human Resources, Accounting, Finance, Information Systems, or a related field

Tools

SAP

Employee Central (SuccessFactors)

Power BI

Job description

PepsiCo Inc. is seeking an Employee Data & Payroll Controls Analyst in the Monterrey area. You will ensure accuracy and synchronization of employee and compensation data across payroll and HR systems, supporting post-payroll controls, data reconciliations, and reporting initiatives to strengthen controls.

You will collaborate with cross-functional teams, analyze high-impact payroll cases, and drive actions to improve data quality and process effectiveness.