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Samson Tanimawo
Samson Tanimawo

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Effective On-Call Rotations: Lessons From Building Fair Schedules

#oncall #sre #devops #culture

The Rotation Nobody Wants

Our on-call rotation was a spreadsheet. Updated manually. Someone always got scheduled during their vacation. Two people occasionally got double-booked. Holidays were a battleground.

Designing Fair Rotations

Principle 1: Equal Burden Distribution

Track total on-call hours, not just shift count:

def calculate_oncall_burden(engineer, period_days=90):
    shifts = get_shifts(engineer, period_days)
    return {
        'total_hours': sum(s.duration_hours for s in shifts),
        'weekend_hours': sum(s.duration_hours for s in shifts if s.is_weekend),
        'holiday_hours': sum(s.duration_hours for s in shifts if s.is_holiday),
        'night_hours': sum(s.duration_hours for s in shifts if s.is_night),
        'pages_received': sum(s.page_count for s in shifts),
        'burden_score': calculate_weighted_score(shifts)
    }

def calculate_weighted_score(shifts):
    """Weight different types of on-call differently."""
    score = 0
    for s in shifts:
        base = s.duration_hours
        if s.is_weekend: base *= 1.5
        if s.is_holiday: base *= 2.0
        if s.is_night: base *= 1.3
        score += base
    return round(score, 1)
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Principle 2: Respect Preferences 

onCall_preferences:
  alice:
    blackout_dates: ["2024-03-25", "2024-04-01:2024-04-05"]  # Vacation
    preferred_days: ["Mon", "Tue", "Wed"]  # Family on weekends
    max_consecutive_days: 3

  bob:
    blackout_dates: ["2024-04-10"]
    preferred_days: ["any"]
    max_consecutive_days: 7
    prefers_weekends: true  # Weekend differential pay
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Principle 3: Minimum Pool Size

The math on sustainable rotations:

Pool size    Frequency           Burnout risk
3 people     1 week on / 2 off   HIGH — unsustainable
4 people     1 week on / 3 off   MEDIUM — barely okay
5 people     1 week on / 4 off   LOW — comfortable
6+ people    1 week on / 5+ off  MINIMAL — ideal

Rule: Minimum 5 people per rotation.
If you have fewer, reduce on-call scope or hire.
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Principle 4: Escalation Tiers 

escalation_chain:
  tier_1:  # Primary on-call
    response_time: 5 minutes
    scope: all pages

  tier_2:  # Secondary on-call (backup)
    response_time: 15 minutes
    scope: escalated or unacknowledged

  tier_3:  # Engineering manager
    response_time: 30 minutes
    scope: P1 only or when both T1+T2 unavailable

  tier_4:  # CTO/VP Engineering
    response_time: 60 minutes
    scope: Extended P1 (>1 hour), customer escalation
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The Override System

Life happens. Make swaps easy:

def request_swap(requesting_engineer, target_date, volunteer=None):
    """Allow easy on-call swaps."""

    if volunteer:
        # Direct swap: Alice asks Bob to cover
        execute_swap(requesting_engineer, volunteer, target_date)
        notify_team(f"{requesting_engineer} swapped with {volunteer} for {target_date}")
    else:
        # Open request: Alice needs coverage, anyone can take it
        post_to_channel(
            f"{requesting_engineer} needs coverage for {target_date}. "
            f"Reply to volunteer. Comp: standard on-call rate."
        )

# Key: NO manager approval needed for swaps
# This reduces friction dramatically
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Holiday Fairness

The holiday rotation is separate and tracked year-over-year:

holidays_2024 = [
    'New Years', 'MLK Day', 'Presidents Day', 'Memorial Day',
    'July 4th', 'Labor Day', 'Thanksgiving', 'Christmas'
]

def assign_holidays(team, year):
    # Get historical holiday assignments
    history = get_holiday_history(team, years=3)

    # Sort by who has covered the FEWEST holidays recently
    sorted_team = sorted(team, key=lambda e: history.get(e, 0))

    assignments = {}
    for i, holiday in enumerate(holidays_2024):
        engineer = sorted_team[i % len(sorted_team)]
        assignments[holiday] = engineer

    return assignments
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Metrics We Track

Metric Target Current
Burden score variance < 15% 8%
Swap request fulfillment > 95% 98%
Pages per shift (average) < 3 1.8
NPS for on-call experience > 0 +32
Holiday coverage fairness < 1 shift variance 0.5

If you want AI-powered on-call scheduling that optimizes for fairness automatically, check out what we're building at Nova AI Ops.

Written by Dr. Samson Tanimawo
BSc · MSc · MBA · PhD
Founder & CEO, Nova AI Ops. https://novaaiops.com

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