When working with automated payroll systems, accuracy is everything. Even a small bug in shift scheduling logic can snowball into major financial discrepancies if not addressed promptly. I encountered such an issue with Paylocity’s scheduler, where repeated shifts led to double charges in payroll. What followed was a meticulous dive into the platform’s scheduling mechanics and a custom deduplication solution to fix the chaos. Here’s a breakdown of what happened, why, and how I resolved it efficiently.
TLDR:
Paylocity’s shift scheduler occasionally created duplicate shift entries, causing employees to be paid twice for the same work period. This stemmed from a loophole in how recurring shifts were translated into the system’s backend. To fix the problem, I implemented a shift deduplication logic that intelligently detects and removes duplicates without affecting valid overlapping shifts. The solution reduced payroll overages and restored scheduling integrity.
The Root of the Problem: Repeated Shifts in Paylocity
Paylocity offers a powerful and flexible scheduler that allows managers to create recurring shift patterns. However, with flexibility came an unexpected bug—certain recurring shift configurations resulted in duplicate shift entries for the same timeslot. Here’s what made this issue particularly tricky:
- The duplicates looked legitimate—same employee, same time, and same day.
- They had different internal IDs, so the system saw them as unique shifts.
- Employees were clocking in only once, but getting paid for both entries.
Imagine this happening across multiple departments for biweekly payroll cycles. The overpayment numbers rapidly spiraled into thousands of dollars—completely unaccounted for in budget forecasts.
Identifying the Conditions that Caused Duplication
After recognizing the situation, my first step was to trace the source of these duplicate shifts. I worked with our HR and IT teams to pull historical shift and payroll data and ran a series of joins to flag any shift entries that shared the same:
- Employee ID
- Shift start and end times
- Shift date
The pattern stood out clearly. These duplicates consistently appeared only on shifts generated with the “recurring weekly” pattern combined with any manual edits after the initial schedule publishing. The issue wasn’t visible during the scheduling review but became apparent when shifts were exported for payroll processing.
How This Affected Payroll
This misalignment between scheduling and payroll modules meant that Paylocity was treating each duplicate individually. Despite employees only clocking in once, raw shift data included multiple scheduled entries for the same period. When the payroll system processed hours worked, it summed both entries, resulting in:
- Double payments for employees
- Inflated payroll reports
- Inaccurate overtime calculations
- Compliance risks for employers
For hourly workers, where even a few extra hours can significantly alter payouts and benefits calculations, this was a serious issue. Not only did it lead to financial waste but also opened up tax and labor compliance concerns.
Designing the Shift Deduplication Logic
Manually fixing the data wasn’t scalable. I decided to build a script that could automatically detect and resolve these duplicates. But the challenge here was nuance—how do you differentiate duplicate scheduled shifts from legitimate overlapping shifts (for example, in training or split shifts)?
To solve this, I created a deduplication engine with the following core logic:
- Grouping: Group shifts by employee ID and shift date.
- Matching Times: Within each group, locate entries that have identical start and end times.
- Validation: Check metadata—was this shift manually added? Did it originate from a recurring series? Was it modified after publishing?
- Priority Rules: Retain the shift with earliest creation date and archive others as duplicates.
- Logging: Maintain audit logs of removed entries for compliance and rollback purposes.
This allowed us to surgically identify and remove only the truly duplicated entries without disrupting legitimate scheduling operations.
Testing the Implementation
To avoid removing valid shifts mistakenly, I set up a test environment using a subset of our employee data. Sample shifts were intentionally duplicated using the same patterns that had caused issues earlier, including:
- Recurring Monday–Friday 9–5 shifts
- Manual edits after schedule publishing
- Back-to-back shifts and overlapping training sessions
The script handled the scenarios gracefully. It removed 100% of the unintended duplicate shifts while leaving valid complexities intact. With confidence in its effectiveness, we moved to roll it out across our production system.
Deployment and Results
The deduplication script was scheduled to run nightly, scanning all newly generated and recently modified shifts. Within the first pay cycle post-implementation, we observed:
- 95% reduction in payroll discrepancies
- No reports of underpayment or missed shifts
- More accurate payroll forecasts and labor cost reports
As an added bonus, Paylocity’s support team took note of the logic and began reviewing their own scheduler module to potentially offer a built-in fix in future releases.
Lessons Learned
This incident offered a powerful reminder that even robust enterprise platforms aren’t immune to edge-case behaviors. More importantly, it highlighted the importance of validating integration points between scheduling and payroll. Here are some of my key takeaways:
- Always run reconciliation checks between clocks and shifts before payroll export.
- Avoid too many manual edits in automated recurring schedules—they’re often a source of hidden bugs.
- Maintain good logging and metadata in shift entries to help trace issues later.
- Invest time in test environments to future-proof against similar issues.
Conclusion
Payroll systems thrive on precision, and even small scheduling anomalies can have outsize impacts. By identifying the duplication bug in Paylocity’s recurring shift handler and building a smart deduplication engine, we not only fixed the problem but also improved long-term scheduling integrity. It’s a good reminder that sometimes the fix isn’t just in code, but in understanding how systems interact beyond their visible UI panels.
As a result, our team is significantly more confident heading into each payroll cycle, with systems in place to alert us long before duplicated shifts have a chance to make their way to paychecks—or spreadsheets.
