On-Call Engineers: Schedules, Compensation & Best Practices
By
Samantha Cox
•
Dec 12, 2025
If you work in tech, you know that system failures don’t wait for business hours. When critical infrastructure goes down at 3 AM, companies need skilled engineers ready to respond immediately. This reality has made on-call engineers an indispensable part of modern technology companies, serving as the first line of defense against costly outages and service disruptions.
47% of companies experience critical outages during non-business hours, with the average cost of system downtime reaching $5,600 per minute in 2024. For technology companies maintaining 99.9% uptime SLAs, having a well-structured on-call process is crucial for survival in competitive markets.
We’ll explore everything you need to know about managing on-call employees effectively, from compensation structures to modern AI-powered hiring solutions. Whether you’re establishing your first on-call team or optimizing an existing program, you’ll find tips that reduce costs, improve reliability, and prevent engineer burnout.
What Are On-Call Engineers and Why Are They Critical?
An on call engineer serves as the designated technical first responder who monitors systems outside of standard business hours and responds to emergencies when critical issues arise. These software engineers maintain responsibility for keeping services running smoothly when most of the team is asleep or away from their desks.
The role has evolved significantly as companies increasingly rely on digital infrastructure for revenue generation. Modern on call responsibilities extend beyond simply fixing broken systems. They include proactive monitoring, incident prevention, and maintaining the high availability that customers expect from digital services.
The business impact of effective on call coverage cannot be overstated. Companies with mature on-call processes report 40% faster incident resolution times and 60% fewer escalations to senior management during off-hours emergencies. This translates directly to improved customer satisfaction, reduced revenue loss, and stronger competitive positioning in markets where reliability matters.
The connection between on call readiness and competitive advantage becomes especially clear when examining customer retention data. Organizations with sub-15-minute response times for critical incidents maintain customer satisfaction scores 25% higher than those with slower response capabilities. In an era where a single negative experience can drive customers to competitors, having engineers on call represents a strategic investment rather than just an operational necessity.
Core Responsibilities of On-Call Software Engineers
The modern on call engineer operates at the intersection of technical expertise and crisis management. Their primary duties during on-call shifts encompass immediate incident response, systematic troubleshooting, and collaborative problem-solving across teams. Expected response times typically fall within 5-15 minutes for critical alerts, requiring engineers to maintain constant connectivity through mobile devices and laptops.
Integration with existing development workflows presents both challenges and opportunities. Most companies structure on-call rotations to complement rather than disrupt sprint cycles, often scheduling engineers for on-call work during periods when their project commitments are lighter. This approach helps maintain productivity while making sure that there is coverage for emergency response.
System Monitoring and Alert Management
Effective on call work begins with robust monitoring systems that provide early warning of potential issues. Engineers on call rely on tools like DataDog, New Relic, and Prometheus to track system health across multiple dimensions. These platforms aggregate metrics from servers, databases, applications, and network infrastructure to create comprehensive visibility into system performance.
Alert prioritization follows standardized severity levels that help engineers on call focus their attention appropriately. P0 alerts indicate critical system failures requiring immediate response, P1 alerts signal high-priority issues that need attention within 30 minutes, and P2 alerts represent medium-priority concerns that can wait until business hours. This structured approach prevents alert fatigue while ensuring genuine emergencies receive prompt attention.
Mobile alert delivery systems like PagerDuty ensure that critical notifications reach the on-call person regardless of location or time of day. Modern implementations include intelligent routing that escalates alerts to secondary responders if the primary engineer doesn’t acknowledge within specified timeframes. Well-configured threshold systems reduce false positive alerts by 60-70%, helping maintain engineer sanity during on-call shifts.
Incident Response and Troubleshooting
When alerts fire, the on call process demands immediate acknowledgment within 5 minutes of notification. This quick response time serves multiple purposes: it prevents automatic escalation to other team members, demonstrates system responsiveness to stakeholders monitoring the situation, and ensures that the responsible person begins working on the problem promptly.
Root cause analysis during active incidents requires proficiency with log aggregation tools like the ELK stack (Elasticsearch, Logstash, Kibana) or Splunk. Engineers on call must quickly sift through thousands of log entries to identify patterns that reveal the underlying cause of system failures. This detective work often occurs under pressure, making familiarity with these tools essential for effective on call performance.
Escalation procedures provide structured paths for involving subject matter experts and senior engineers when initial troubleshooting efforts prove insufficient. Most companies establish clear escalation policy guidelines that specify when to engage additional resources and how to coordinate response efforts across other teams. Communication protocols during incidents ensure that stakeholders receive regular updates on progress and estimated resolution timelines.
Documentation requirements for post incident analysis create valuable knowledge repositories that benefit future incident responses. Engineers on call typically document their troubleshooting steps, findings, and resolution methods in shared systems where other teams can access this information. This practice transforms individual learning into organizational knowledge that improves overall system reliability.
Post-Incident Analysis and Improvement
The on call responsibilities extend beyond immediate problem resolution to include systematic analysis designed to prevent future incidents. Conducting blameless post-mortems within 48 hours of major incidents helps teams identify root causes without creating a culture of finger-pointing. These sessions focus on understanding system failures rather than individual mistakes, encouraging honest discussion about improvement opportunities.
Creating actionable items represents the most valuable outcome of post incident analysis. Effective post-mortems generate an average of 3-5 specific action items that address the underlying conditions that enabled the incident to occur. These might include code changes, infrastructure improvements, monitoring enhancements, or process modifications that reduce the likelihood of similar problems.
Knowledge sharing through internal wikis and team meetings ensures that lessons learned during on-call shifts benefit the broader engineering organization. Many companies schedule monthly sessions where engineers on call present their most interesting cases and discuss resolution strategies. This practice helps build collective expertise while reducing the knowledge silos that can develop around complex systems.
Updating runbooks and standard operating procedures represents ongoing maintenance that improves future on call effectiveness. As systems evolve and new failure modes emerge, the documentation that guides emergency response must evolve accordingly. Engineers on call often contribute directly to these updates based on their hands-on experience with real incidents.
On-Call Schedule Types and Rotation Models
Choosing the right scheduling approach depends heavily on team size, geographic distribution, and coverage requirements. Companies typically need 2-4 weeks to establish new rotation patterns and observe their effectiveness in practice. The goal involves balancing adequate coverage with sustainable workloads that prevent engineer burnout and maintain code quality during regular development work.
Different scheduling models offer distinct advantages and trade offs that align with specific organizational needs. Smaller teams often start with simpler rotation patterns and evolve toward more sophisticated approaches as they grow and gain experience with on-call operations.
Primary/Secondary On-Call Schedules
The two-tier system represents the most common approach to on-call coverage, featuring a primary engineer who receives initial alerts and a secondary engineer who serves as backup when the primary person is unavailable. This model provides redundancy while maintaining clear ownership of incidents. Typical rotation schedules involve 1 week as primary on call, 1 week as secondary on call, followed by 2 weeks without on-call responsibilities.
Primary engineers handle approximately 95% of incidents in well-managed teams, with secondary engineers stepping in primarily when the primary person cannot respond within escalation timeouts. The escalation timeout usually ranges from 10-15 minutes, providing adequate time for the primary engineer to acknowledge alerts while ensuring rapid response when they’re truly unavailable.
This rotation model works particularly well for teams with 6-12 members, providing sufficient coverage while preventing excessive on call burden for any individual. The predictable schedule allows engineers to plan personal activities around their on-call weeks and mentally prepare for potential nighttime interruptions.
Follow-the-Sun Coverage Model
Global technology companies increasingly adopt follow-the-sun coverage models that provide 24/7 incident response capability while minimizing the personal impact on individual engineers. This approach requires teams distributed across at least three major time zones (typically APAC, EMEA, and Americas) to provide coverage during each region’s business hours.
Handoff procedures between regions become critical success factors in this model. The outgoing team in one time zone must communicate system status, ongoing issues, and any special concerns to the incoming team in the next region. These handoffs typically occur during brief overlap periods designed to ensure continuity of coverage without requiring anyone to work outside normal hours.
The primary advantage involves eliminating middle-of-night wake-ups for engineers, significantly reducing the stress and fatigue associated with traditional on call work. However, implementation requires substantial coordination across geographically distributed teams and clear communication processes that maintain effectiveness across cultural and linguistic differences.
Expert Escalation Tiers
Large organizations often implement multi-tier escalation systems that match incident complexity with appropriate expertise levels. L1 engineers handle general system issues, L2 engineers address system-specific problems requiring deeper knowledge, and L3 engineers tackle architectural-level concerns that require senior expertise.
Response time expectations vary by tier: L1 engineers respond within 5 minutes, L2 engineers respond within 15 minutes, and L3 engineers respond within 30 minutes. These timeframes account for the increasing specialization and seniority of engineers at higher tiers, who may be less immediately available but bring critical expertise to complex problems.
Rotation frequency typically extends to monthly periods for expert tiers, acknowledging that these roles require significant time investment and deep system knowledge. This longer rotation helps ensure that L2 and L3 engineers can maintain their primary development responsibilities while providing specialized on call support.
On-Call Compensation Models and Industry Standards
Determining appropriate on-call pay requires balancing multiple factors including market rates, internal equity, legal compliance, and employee satisfaction. The 2025 market shows significant variation based on company size, geographic location, and industry sector. Understanding these dynamics helps organizations design compensation packages that attract talent while managing costs effectively.
Legal considerations under the Fair Labor Standards Act add complexity for companies with non-exempt employees, requiring careful tracking of hours worked during on-call shifts and appropriate overtime compensation. Many organizations structure on-call roles as exempt positions to simplify administration, though this approach requires meeting specific salary and responsibility thresholds.
The impact on total compensation packages extends beyond direct on call payments to include considerations like work-life balance, career development opportunities, and overall job satisfaction. Companies that excel at on-call management often find it easier to recruit top talent even when their base salaries are competitive rather than market-leading.
Compensation Model | Weekly Base | Incident Bonus | Total Annual | Best For |
Small Startup | $200-400 | $50-100 | $12,000-25,000 | Teams < 20 engineers |
Mid-Size Company | $300-600 | $75-150 | $18,000-40,000 | Teams 20-100 engineers |
Large Enterprise | $500-800 | $100-200 | $30,000-55,000 | Teams > 100 engineers |
Premium Tech | $600-1000 | $150-300 | $40,000-70,000 | FAANG-level companies |
On-Call Stipend Programs
Fixed weekly or monthly payments provide predictable compensation regardless of incident volume, helping engineers budget their finances while acknowledging the availability requirement inherent in on-call work. Industry ranges typically span $200-800 per week for primary on-call duties, with amounts varying based on company size, location, and competitiveness of the overall compensation package.
Additional per-incident bonuses recognize the extra effort required to respond to actual emergencies, especially during nights and weekends. These bonuses typically range from $50-200 for after-hours responses, with higher amounts for incidents that require extensive troubleshooting or coordination with multiple teams.
Tax implications require careful consideration since on-call pay often qualifies as supplemental income subject to different withholding rates. Benefits administration becomes more complex when stipend programs interact with other compensation elements like equity grants or performance bonuses. Most companies work with payroll specialists to ensure compliant handling of these payments.
Salary Integration Models
Some organizations prefer integrating on-call responsibilities into base salaries rather than maintaining separate stipend programs. This approach typically involves salary increases of 10-20% for roles that include regular on call duties. The integration simplifies administration while acknowledging that on call responsibilities represent a significant additional job requirement.
Time-off compensation provides another approach to acknowledging on-call burden. Some companies offer 1 day off per week on call duty, allowing engineers to recover from sleep disruption and maintain work-life balance. This comp time often proves more valuable to employees than equivalent monetary compensation.
Flexible work arrangements frequently accompany on-call responsibilities, recognizing that engineers who respond to 3 AM alerts might benefit from delayed start times the following morning. Remote work options become especially important for on-call teams, ensuring that engineers can respond effectively regardless of physical location.
Performance review considerations require careful attention to ensure that on call participation receives appropriate recognition in career advancement decisions. Many companies incorporate on-call effectiveness metrics into engineering performance evaluations, acknowledging this work as a valuable contribution to organizational success.
Best Practices for Effective On-Call Management
Implementing sustainable on-call processes requires systematic attention to multiple dimensions including technology, training, and culture. Companies that excel at on-call management typically invest in comprehensive programs that address both immediate operational needs and long-term team sustainability.
The implementation roadmap for establishing on-call processes generally spans 2-3 months, allowing time for tool selection, training development, and gradual transition from existing practices. Success metrics and KPIs provide objective measures of program effectiveness, helping organizations identify areas for continuous improvement.
Change management strategies become essential when introducing on-call responsibilities to teams that haven’t previously shared these duties. Clear communication about expectations, compensation, and career benefits helps build buy-in from engineers who may initially resist additional responsibilities.
Alert Optimization and Fatigue Prevention
Maintaining an 80-90% actionable alert ratio represents a critical success factor in preventing alert fatigue among engineers on call. This metric measures the percentage of alerts that require genuine human intervention versus false alarms or automatically resolved issues. Achieving this ratio requires careful threshold tuning and intelligent alert correlation.
Automated remediation for common issues like disk space cleanup and memory leak recovery can eliminate 40-60% of routine alerts that otherwise interrupt engineers during on-call shifts. These automation scripts typically handle well-understood problems that follow predictable resolution patterns, freeing human attention for complex troubleshooting tasks.
Intelligent alert grouping prevents notification storms that can overwhelm on-call engineers during widespread system issues. Modern monitoring platforms can recognize related alerts and group them into single notifications, reducing the number of individual alerts from potentially hundreds to manageable dozens during major incidents.
Regular alert hygiene reviews conducted monthly or quarterly help maintain alert quality over time. These reviews examine alert frequency, resolution patterns, and false positive rates to identify opportunities for threshold adjustments or automation improvements. Teams that conduct systematic reviews report 30-50% reductions in non-actionable alerts over 6-month periods.
Documentation and Knowledge Management
Runbook templates provide standardized formats for step-by-step troubleshooting guides that help engineers on call respond consistently to common issues. Effective runbooks include clear decision trees, command examples, and escalation criteria that enable rapid problem resolution even when the on-call engineer lacks deep expertise in the affected system.
Service dependency mapping and system architecture diagrams help on-call engineers understand the relationships between different components during complex incidents. These visual aids become especially valuable during high-pressure situations when engineers need to quickly assess the potential impact of system changes or identify upstream causes of failures.
Contact lists with escalation paths and expert availability ensure that on call engineers can rapidly engage subject matter experts when incidents require specialized knowledge. These lists typically include multiple contact methods, expertise areas, and availability schedules to facilitate quick decision-making about when and how to escalate.
Historical incident databases with searchable solutions transform organizational learning into practical resources that improve response effectiveness. Well-maintained databases allow engineers on call to quickly search for similar past incidents and review successful resolution strategies, reducing mean time to resolution by 20-30% for recurring problems.
Training and Onboarding Programs
Shadowing periods typically spanning 2-4 weeks provide new engineers with hands-on exposure to on-call responsibilities before they take independent duty. During shadowing, experienced engineers guide newcomers through real incidents while explaining decision-making processes and troubleshooting techniques. This mentorship approach builds confidence while ensuring knowledge transfer.
Simulation exercises using chaos engineering principles create controlled opportunities for engineers to practice incident response without risking production systems. These exercises often reveal gaps in documentation or procedures that can be addressed before they impact real emergencies. Teams that conduct regular simulations report higher confidence levels and faster response times during actual incidents.
Cross-training on adjacent systems and services helps engineers on call understand the broader context of the applications they support. This knowledge proves valuable when incidents span multiple systems or when troubleshooting requires understanding upstream and downstream dependencies. Cross-training typically includes both formal sessions and informal knowledge sharing opportunities.
Mentorship programs pairing junior and senior engineers provide ongoing support that extends beyond initial training periods. These relationships help newer engineers build expertise while providing senior engineers with opportunities to share knowledge and develop leadership skills. Effective mentorship programs often result in improved retention and faster skill development across the team.
Common Technical Challenges and Modern Solutions
Understanding the most frequent on-call incident types helps teams prepare for common scenarios while investing in preventive measures that reduce overall incident volume. Analysis of 2025 incident data reveals consistent patterns across different organizations, providing insights that inform both immediate response strategies and longer-term infrastructure improvements.
Technology solutions continue evolving to reduce both incident frequency and resolution time through automation, predictive analytics, and improved tooling. The return on investment for infrastructure improvements often exceeds 300% when calculated across reduced incident costs, improved engineer productivity, and enhanced system reliability.
Modern solutions increasingly use artificial intelligence and machine learning to predict potential issues before they impact production systems. These predictive capabilities enable proactive intervention that prevents incidents rather than simply responding to them after they happen.
System Reliability Issues
Database performance degradation accounts for approximately 32% of on call incidents, making it the single most common category of emergency response. These incidents often involve slow queries, connection pool exhaustion, or storage capacity issues that require immediate attention to restore functionality. Engineers on call must quickly identify whether problems stem from application code, database configuration, or infrastructure capacity.
Network connectivity problems represent 18% of incidents, typically manifesting as intermittent timeouts, DNS resolution failures, or bandwidth saturation. These issues require systematic troubleshooting to isolate whether problems exist in local networks, internet service providers, or cloud infrastructure providers. The distributed nature of modern applications makes network debugging particularly challenging during late-night emergency responses.
Third-party service dependencies cause 15% of on-call incidents, highlighting the risks associated with external APIs, payment processors, and cloud services. When third-party providers experience outages, engineers on call must quickly implement workarounds or failover procedures to maintain service availability. These incidents often require communication with external support teams who may not respond immediately.
Application deployment failures account for 12% of incidents, usually occurring when new code releases introduce bugs or configuration errors. Engineers on call need to quickly determine whether issues require immediate rollback, hot fixes, or other interventions. Having clear rollback procedures and automated deployment tools significantly reduces the stress and complexity of these responses.
Security-related alerts and breach responses comprise 8% of incidents but often require the most complex coordination between engineering, security, and management teams. These incidents may involve suspicious access patterns, potential data breaches, or malicious attacks that require immediate containment. Engineers on call must balance rapid response with careful evidence preservation for subsequent analysis.
Modern Tooling and Automation
AI-powered incident prediction and prevention systems analyze historical patterns, system metrics, and deployment data to identify conditions that precede common failure modes. These systems can alert teams to potential issues hours or days before they impact production, enabling proactive maintenance that prevents customer-facing outages. Early adopters report 40-60% reductions in emergency incident volume after implementing predictive systems.
Automated rollback capabilities for deployment failures enable rapid restoration of service when new releases introduce problems. Modern CI/CD pipelines can automatically detect deployment health issues and trigger rollbacks without human intervention, often completing the process in under 5 minutes. This automation proves especially valuable during overnight deployments when engineering teams have limited availability.
Self-healing infrastructure using Kubernetes and cloud auto-scaling can automatically respond to many common infrastructure issues without human intervention. These systems can restart failed containers, scale capacity in response to load increases, and failover to backup systems when primary infrastructure becomes unavailable. Self-healing capabilities typically resolve 50-70% of infrastructure-related issues without requiring engineer involvement.
ChatOps integration for collaborative incident response brings together communication platforms like Slack with operational tools to streamline coordination during complex incidents. Engineers can execute commands, query system status, and coordinate with other teams directly from chat interfaces, reducing the time spent switching between different tools during high-pressure situations. Teams using ChatOps report 25-40% faster incident resolution times.
How AI is Revolutionizing On-Call Engineering and Hiring
The technology industry faces urgent hiring challenges that directly impact on call capability and overall system reliability. Statistics show that 73% of tech companies struggle to fill on call positions, creating coverage gaps that increase risk and engineer burnout. The connection between on call competency and overall engineering capability makes these hiring challenges particularly critical for organizational success.
Traditional hiring processes often fail to adequately assess candidates’ readiness for on-call responsibilities, focusing instead on coding skills and algorithm knowledge. This gap between assessment and actual job requirements leads to expensive hiring mistakes and new engineer struggles when they encounter real production incidents.
The role of artificial intelligence in identifying, screening, and developing on call engineers offers promising solutions to these persistent challenges. AI systems can analyze behavioral patterns, technical competencies, and stress management capabilities that predict success in high-pressure operational roles.
Fonzi’s Multi-Agent AI for On-Call Talent Acquisition
Automated screening for on-call readiness and incident response skills represents a significant advancement over traditional interview processes. Fonzi’s multi-agent AI system evaluates candidates across multiple dimensions including technical knowledge, problem-solving approach, and stress management capabilities. This comprehensive assessment provides much more accurate predictions of on call engineer success than conventional hiring methods.
Behavioral assessment algorithms identify stress management capabilities by analyzing candidate responses to simulated high-pressure scenarios. The system can detect communication patterns, decision-making speed, and emotional regulation that correlate with effective incident response. These assessments prove particularly valuable since on-call work often occurs during stressful situations that traditional interviews don’t replicate.
Technical competency evaluation through simulated incident scenarios provides realistic assessment of candidate abilities without requiring access to production systems. Candidates work through realistic troubleshooting exercises that mirror common on-call situations, allowing evaluation of their systematic approach to problem-solving and familiarity with standard tools and procedures.
Predictive modeling for on call engineer success and retention rates helps organizations make better hiring decisions while reducing turnover costs. By analyzing patterns from successful engineers and correlating them with candidate characteristics, Fonzi’s system can predict which candidates are most likely to thrive in on-call roles and remain with the company long-term.
Integration with existing ATS and HR systems ensures seamless workflow integration without disrupting established hiring processes. The AI system provides enhanced candidate insights while working within familiar tools and procedures, making adoption easier for recruiting teams and hiring managers.
Streamlining On-Call Team Management with AI
Intelligent scheduling optimization considers engineer preferences, workload distribution, and historical performance to create on-call rotations that maximize coverage while minimizing burnout. The AI system can account for factors like upcoming deadlines, personal time requests, and recent incident response volume to create schedules that balance fairness with operational effectiveness.
Automated performance tracking and feedback generation provides objective assessment of on call engineer effectiveness without requiring manual management overhead. The system analyzes response times, resolution quality, and collaboration effectiveness to generate personalized feedback that helps engineers improve their skills while providing managers with data-driven insights.
Predictive burnout detection and prevention recommendations analyze workload patterns, incident frequency, and engineer feedback to identify individuals at risk of burnout before problems become severe. Early intervention strategies can include schedule adjustments, additional support, or training opportunities that address specific stress factors.
Real-time skill gap analysis and training program suggestions help managers identify areas where team capabilities could be strengthened. The AI system can recommend specific training programs, cross-training opportunities, or hiring priorities based on incident patterns and team performance data.
Cost optimization through AI-driven compensation modeling ensures that on-call pay structures remain competitive while controlling expenses. The system can analyze market data, internal equity considerations, and performance metrics to recommend compensation adjustments that maintain talent while optimizing budget allocation.
Building Confidence in AI-Powered Hiring Solutions
Success stories from companies using AI for on call engineer hiring demonstrate measurable improvements in hiring accuracy, time-to-hire, and long-term retention. Early adopters report 40-60% reduction in time-to-hire for on call roles while achieving higher success rates for new engineer integration into existing teams.
Data security and privacy considerations require careful attention when implementing AI hiring tools. Responsible AI systems maintain strict data protection protocols while providing transparency about how candidate information is used and stored. Organizations must ensure compliance with applicable privacy regulations while gaining the benefits of AI-enhanced assessment.
Implementation roadmap for adopting AI in technical recruitment typically spans 3-6 months, including system integration, team training, and gradual rollout to ensure smooth transition. Successful implementations often start with pilot programs that demonstrate value before expanding to full-scale deployment across all technical hiring.
ROI analysis consistently shows significant returns from AI-powered hiring solutions, with reduced hiring costs, improved retention, and better job performance generating value that far exceeds technology investments. Companies typically see positive ROI within 6-12 months of implementation when factoring in reduced turnover and improved operational effectiveness.
Change management strategies for HR and engineering teams focus on education about AI capabilities while addressing concerns about automation replacing human judgment. Successful implementations emphasize AI as augmenting rather than replacing human decision-making, providing better data to support existing hiring processes.
Conclusion
Effective on call management represents a critical capability for technology organizations competing in today’s always-on digital economy. The combination of proper scheduling, fair compensation, comprehensive training, and modern tooling creates sustainable programs that protect both system reliability and engineer well-being.
The integration of artificial intelligence into on-call operations and hiring processes offers unprecedented opportunities to optimize these programs while addressing persistent talent acquisition challenges. Companies that embrace AI-powered solutions like Fonzi’s multi-agent platform gain significant advantages in building high-performing on-call teams that drive competitive differentiation.
As technology systems grow increasingly complex and customer expectations for reliability continue rising, organizations must evolve their approach to on call management. The companies that invest in comprehensive programs today will be best positioned to maintain the operational excellence that defines market leaders in the digital economy.
Ready to revolutionize your on-call hiring process? Book a call to learn how Fonzi’s AI-powered platform can help you identify, assess, and hire exceptional on-call engineers who drive organizational success while maintaining work-life balance.
