Amazon
Launched a hiring workflow automation to improve consistency in the candidate experience
ROLE
Program Manager,
Global Recruiting Operations
SCOPE
3 months
Global
SKILLS
Process Design
Automation Strategy
Risk MitigationData Quality & Systems Thinking
PARTNERS
Data Analyst
HR Policy Owners
Recruiting Operations Leaders
Data Platform Teams
THE CHALLENGE
Designing an automated system to improve reliability and reduce candidate issues.
I was responsible for a global recruiting policy that ran on a slow, manual workflow spanning four systems and a massive Excel tracker. Every day it processed thousands of applications, and errors generated 50+ support tickets per week, involving recruiters, candidates, HR partners, and Operations leaders. At the same time, one of the core platforms was being sunset, and I had just three months to redesign the workflow.
USERS & MOTIVATIONS
Manual operations were causing downstream issues for recruiters and candidates.
The primary users were recruiting operations teams who carried the manual burden of applying this policy. Errors in the complex workflow created downstream effects for recruiters and candidates, leading to escalations and poor candidate experience. The goal was to reduce operational effort while improving consistency and preventing those downstream failures.
Primary:
Recruiting operations teams
Gather data reliably across multiple systems
Apply complex policy logic consistently
Reduce repetitive manual work
Secondary:
Recruiters & candidates
Fewer workflow errors that triggered escalations
Minimal disruption to recruiting and candidate experience
Clear, consistent eligibility decisions
DEFINING SUCCESS
Aligned on success metrics early to guide trade-offs and prioritization.
Manual hours saved to measure operations team effort reduction
Policy coverage % to identify missed eligibility cases
Error rate to protect compliance and candidate experience
Support tickets to improve candidate experience
KEY TRADE-OFF
The core tension was automating quickly while preserving accuracy.
Given a three-month timeline, limited engineering support, and inconsistent data quality, I prioritized the initial automation on high-volume workflow items with reliable data sources. I preserved more complex scenarios for human review, as automating them prematurely would have increased errors and damaged the candidate experience.
THE SOLUTION
Transformed legacy logic to an automated tool.
I worked closely with a data analyst to translate the existing policy logic into technical requirements and a rules-based automated workflow.
Simplified legacy logic
Partnered with HR and Operations to remove unnecessary rules, keeping only what was critical. Worked closely with the analyst to provide business context, empowering them to propose more purpose-built logic.
Built for stability
Replaced fragile org hierarchy and alias-based logic with stable, data-driven identifiers that wouldn’t break with business changes.
Validated outcomes
Built an Excel-based testing suite to check outcomes, track defects, and troubleshoot with the data analyst before launch.
OUTCOMES
Delivered clear impact in three months with room to scale.
I delivered the automated tool in under three months, significantly reducing operational effort, improving coverage and consistency for candidates, and building a flexible framework that can support future policy and operational changes.
1,000+ manual hours saved annually
30% increase in policy coverage
< .05% compliance error rate
60% reduction in support tickets
KEY LEARNING
Moving fast doesn’t have to mean breaking things.
This project was a meaningful lesson in balancing urgency with quality. By being intentional about prioritization, testing, and communication, I delivered a reliable system under pressure, while laying the groundwork for long-term scalability and flexibility to changing business requirements.
WHAT I WOULD DO DIFFERENTLY
There’s always room to improve...
Looking back on my learnings from this project, I would have automated more edge cases during the three-month project, building audit trails to catch errors. This approach would have allowed the system to improve incrementally without heavy technical support. Because priorities shifted post launch, I didn’t have the resources to progress the roadmap, and some technical debt remained.
Amazon
Launched a hiring workflow automation to improve consistency in the candidate experience
ROLE
Program Manager,
Global Recruiting Operations
SCOPE
3 months
Global
SKILLS
Process Design
Automation Strategy
Risk MitigationData Quality & Systems Thinking
PARTNERS
Data Analyst
HR Policy Owners
Recruiting Operations Leaders
Data Platform Teams
THE CHALLENGE
Designing an automated system to improve reliability and reduce candidate issues.
I was responsible for a global recruiting policy that ran on a slow, manual workflow spanning four systems and a massive Excel tracker. Every day it processed thousands of applications, and errors generated 50+ support tickets per week, involving recruiters, candidates, HR partners, and Operations leaders. At the same time, one of the core platforms was being sunset, and I had just three months to redesign the workflow.
USERS & MOTIVATIONS
Manual operations were causing downstream issues for recruiters and candidates.
The primary users were recruiting operations teams who carried the manual burden of applying this policy. Errors in the complex workflow created downstream effects for recruiters and candidates, leading to escalations and poor candidate experience. The goal was to reduce operational effort while improving consistency and preventing those downstream failures.
Primary:
Recruiting operations teams
Gather data reliably across multiple systems
Apply complex policy logic consistently
Reduce repetitive manual work
Secondary:
Recruiters & candidates
Fewer workflow errors that triggered escalations
Minimal disruption to recruiting and candidate experience
Clear, consistent eligibility decisions
DEFINING SUCCESS
Aligned on success metrics early to guide trade-offs and prioritization.
Manual hours saved to measure operations team effort reduction
Policy coverage % to identify missed eligibility cases
Error rate to protect compliance and candidate experience
Support tickets to improve candidate experience
KEY TRADE-OFF
The core tension was automating quickly while preserving accuracy.
Given a three-month timeline, limited engineering support, and inconsistent data quality, I prioritized the initial automation on high-volume workflow items with reliable data sources. I preserved more complex scenarios for human review, as automating them prematurely would have increased errors and damaged the candidate experience.
THE SOLUTION
Transformed legacy logic to an automated tool.
I worked closely with a data analyst to translate the existing policy logic into technical requirements and a rules-based automated workflow.
Simplified legacy logic
Partnered with HR and Operations to remove unnecessary rules, keeping only what was critical. Worked closely with the analyst to provide business context, empowering them to propose more purpose-built logic.
Built for stability
Replaced fragile org hierarchy and alias-based logic with stable, data-driven identifiers that wouldn’t break with business changes.
Validated outcomes
Built an Excel-based testing suite to check outcomes, track defects, and troubleshoot with the data analyst before launch.
OUTCOMES
Delivered clear impact in three months with room to scale.
I delivered the automated tool in under three months, significantly reducing operational effort, improving coverage and consistency for candidates, and building a flexible framework that can support future policy and operational changes.
1,000+ manual hours saved annually
30% increase in policy coverage
< .05% compliance error rate
60% reduction in support tickets
KEY LEARNING
Moving fast doesn’t have to mean breaking things.
This project was a meaningful lesson in balancing urgency with quality. By being intentional about prioritization, testing, and communication, I delivered a reliable system under pressure, while laying the groundwork for long-term scalability and flexibility to changing business requirements.
WHAT I WOULD DO DIFFERENTLY
There’s always room to improve...
Given the chance to do it all again, I would have automated more edge cases during the three-month project and built audit trails to catch errors. This approach would have allowed the system to improve incrementally after the initial launch, without heavy technical support. Because priorities shifted post launch, I didn’t have the resources to progress the roadmap, and some technical debt remained.