Software Development Engineer in Test

📋 Description

• • Design and implement automated functional health checks for disaster recovery (DR) and production environments using synthetic transactions and API validation to ensure system availability and correctness.
• • Build continuous validation pipelines that verify end-to-end business workflows including authentication, transaction processing, and third-party system integrations under real-world conditions.
• • Develop intelligent alerting mechanisms that prioritize functional failures and customer-impacting behavior over generic infrastructure metrics to reduce noise and improve incident response accuracy.
• • Integrate observability signals—including logs, metrics, and distributed traces—with automated test frameworks to enhance system visibility and accelerate root cause analysis during outages.
• • Create AI/ML-driven systems to detect recurring failure patterns, correlate anomalies across microservices, and identify probable root causes of system degradation or downtime.
• • Engineer automated remediation systems capable of recommending or initiating self-healing actions to reduce mean time to recovery and improve system resilience.
• • Partner cross-functionally with QA, SRE, and Engineering teams to align on service reliability goals, refine incident response playbooks, and enhance disaster recovery readiness.
• • Define, measure, and report on functional SLAs, service health indicators, and quality metrics that reflect customer-facing system performance rather than internal technical benchmarks.
• • Contribute to regular disaster recovery drills and automated validation exercises to test and improve the robustness of Socure’s production systems under failure conditions.
• • Champion the shift from traditional QA practices toward autonomous, data-driven quality systems that proactively validate system integrity without manual intervention.
• • Maintain and evolve automated test frameworks using tools such as Playwright, Jest, and SuperTest to ensure consistent, scalable, and maintainable validation across complex distributed architectures.
• • Translate production incident data into actionable test cases and validation criteria that prevent recurrence and improve long-term system reliability.
• • Evaluate and implement cloud-based monitoring platforms including Datadog, New Relic, CloudWatch, and Splunk to enrich test outcomes with real-time operational insights.
• • Apply systems thinking to understand failure modes in microservices environments and design validation strategies that account for network latency, service dependencies, and partial outages.
• • Contribute to CI/CD pipelines by embedding automated validation gates that prevent unstable code from advancing to production environments.
• • Document validation architecture, test coverage, and reliability metrics to enable transparency and knowledge sharing across engineering teams.