Quality Engineering Manager

About the role

The Technical Engineer serves as a senior production support and reliability engineering professional responsible for ensuring the availability, stability, performance, and operational excellence of critical business applications and platforms. This role combines strong troubleshooting expertise with modern Site Reliability Engineering (SRE), observability, automation, cloud operations, and incident management practices. The Technical Engineer partners with Engineering, Architecture, Infrastructure, Security, Product, and Vendor teams to proactively identify operational risks, improve system resilience, accelerate incident resolution, and continuously enhance customer and employee experiences. The ideal candidate possesses deep technical knowledge of application support, distributed systems, cloud technologies, monitoring platforms, automation tools, and modern operational practices, and is passionate about eliminating repetitive work through automation and leveraging AI‑powered tools.

Responsibilities

• Production Support & Incident Management – Serve as a technical escalation point for critical production incidents, outages, and service degradation events. Lead troubleshooting and root cause analysis efforts across applications, integrations, infrastructure, cloud services, APIs, and supporting technologies. Coordinate incident response activities involving application teams, infrastructure teams, vendors, and business stakeholders. Restore service quickly while ensuring long‑term corrective actions are identified and implemented. Participate in major incident management processes and post‑incident reviews.
• Site Reliability Engineering (SRE) – Apply SRE principles to improve platform reliability, scalability, resilience, and operational efficiency. Define and support Service Level Indicators (SLIs), Service Level Objectives (SLOs), and operational performance metrics. Drive reduction of operational toil through automation and process improvement. Support production readiness reviews and operational acceptance processes. Participate in disaster recovery, resiliency, failover, and business continuity testing.
• Troubleshooting & Problem Management – Analyze complex system behavior using logs, metrics, traces, performance data, and monitoring tools. Perform deep technical investigations across application, infrastructure, data, network, and cloud environments. Identify recurring issues, trends, and systemic problems to reduce future incidents. Lead root cause analysis (RCA) activities and implement preventive solutions. Develop technical recommendations that improve system stability, performance, and reliability.
• Observability & Monitoring – Design, implement, and optimize monitoring, alerting, logging, and observability solutions. Develop dashboards and health indicators providing visibility into application and platform performance. Partner with engineering teams to improve observability through instrumentation, distributed tracing, synthetic monitoring, and telemetry collection. Continuously refine alerting strategies to reduce false positives and alert fatigue. Establish operational health metrics and reliability reporting.
• Automation & Scripting – Develop and maintain automation solutions that improve operational efficiency and service reliability. Create scripts, tools, and workflows to automate diagnostics, health checks, remediation activities, and routine support tasks. Leverage Infrastructure as Code (IaC) and automation frameworks where appropriate. Drive continuous improvement through operational automation and self‑healing capabilities. Partner with engineering teams to integrate automation into deployment and operational workflows. Desired scripting technologies: PowerShell, Python, Bash/Shell, SQL, REST APIs, workflow automation platforms.
• AI‑Assisted Operations & Innovation – Leverage AI and Generative AI tools to improve incident analysis, troubleshooting, knowledge management, and operational efficiency. Utilize AI‑powered operational insights to identify patterns, anomalies, and emerging risks. Contribute to development of intelligent support capabilities including chatbots, operational copilots, automated RCA generation, and knowledge recommendations. Evaluate opportunities to improve production support through AI‑enabled automation and predictive analytics. Promote responsible AI practices