Job Details

Retirement Analyst

  2025-09-23     Agility Recruiting     San Mateo,CA  
Description:

STRS Correction Specialist - Temporary Position

Duration: 6-8 months, Full-time
Position Overview

We are seeking an experienced STRS specialist to join our team for a critical data correction project. This role involves correcting historical teacher retirement reporting errors from a three-year retro pay implementation, ensuring accurate STRS compliance for faculty and adjunct staff.

Key Responsibilities

  • Review and correct STRS payroll and pay rate data errors for approximately 400 faculty members and several hundred adjunct staff
  • Analyze Excel spreadsheets containing historical pay data that was incorrectly consolidated
  • Separate and correct multiple pay rates and lines for each employee based on established business rules
  • Input corrected data into Banner ERP system following district protocols
  • Utilize STRS retirement website for reporting and error resolution
  • Maintain detailed records of corrections and progress
  • Collaborate with payroll and HR teams to resolve complex cases

Required Qualifications

  • 3+ years experience with STRS retirement reporting and corrections
  • Working knowledge of STRS retirement website, specifically the retirement reporting section
  • Experience identifying and correcting payroll and pay rate data errors
  • Strong Excel skills for data analysis and manipulation
  • Detail-oriented with high accuracy in data entry
  • Ability to work independently while knowing when to seek guidance

Preferred Qualifications

  • Banner ERP system experience
  • California education payroll background
  • Experience with teacher retro pay situations
  • Knowledge of STRS business rules and compliance requirements

Work Environment

  • Professional, collaborative district office setting
  • Casual dress code
  • Ample parking available
  • Modern office equipment and technology

Note: The STRS website will be undergoing changes during this assignment period. The ideal candidate should be adaptable and comfortable learning new system features as they are implemented.


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