Principal Engineer Automation

In This Role, You Will

Software Architecture and Engineering

• Act as a trusted technical advisor to senior leadership, influencing the architecture and development of applications, platforms, APIs, network automation services, information security capabilities, data systems, operating environments, and cloud-native technologies for highly complex business and technical needs across multiple organizations.

• Lead the strategy and resolution of highly complex and unique engineering challenges requiring evaluation across multiple technology domains, delivering solutions that are long-term, large-scale, secure, resilient, and maintainable.

• Design and develop production-grade software platforms, APIs, microservices, SDKs, libraries, automation frameworks, and reusable components that enable network and infrastructure engineering capabilities across the enterprise.

• Define software architectures for distributed, event-driven, and asynchronous systems, including service boundaries, data contracts, workflow states, integration patterns, failure handling, concurrency, consistency, and recovery models.

• Establish software engineering standards for application structure, API design, code quality, automated testing, secure development, dependency management, versioning, release engineering, observability, and production readiness.

• Develop reference implementations and contribute directly to high-value or technically complex portions of the platform, particularly where new patterns, technologies, or engineering standards must be proven.

• Lead technical design reviews, architecture reviews, code reviews, failure-mode analysis, and production-readiness assessments for critical platform capabilities.

Platform Engineering and Developer Experience

• Build and evolve internal engineering platforms that provide self-service automation, standardized workflows, reusable services, governed execution paths, and consistent developer experiences.

• Treat shared engineering platforms as products, with clearly defined users, service contracts, roadmaps, adoption measures, documentation, support models, and reliability objectives.

• Create paved roads and golden paths that enable engineering teams to develop, test, certify, release, and operate automation through approved patterns rather than one-off implementations.

• Improve developer productivity through reusable APIs, templates, software development kits, CI/CD pipelines, test harnesses, local development environments, documentation, and automated onboarding.

• Reduce duplicated engineering effort and operational toil by converting common functions into reusable platform services, shared libraries, automation modules, and supported integration patterns.

• Establish appropriate boundaries among platform ownership, application ownership, production execution, operational support, and risk decision-making.

Workflow Orchestration and Automation

• Design durable workflows for long-running, failure-prone, approval-dependent infrastructure and network processes using Temporal, Celery, or comparable workflow and asynchronous execution technologies.

• Define patterns for workflows, activities, workers, task queues, events, signals, timers, retries, timeouts, compensating actions, versioning, idempotency, replay safety, and human approval gates.

• Build workflow capabilities that preserve state across failures, support controlled resumption, provide complete execution history, and maintain alignment among technical validation, business approval, and production execution.

• Create reusable workflow components for intake, validation, certification, release approval, change alignment, deployment, verification, rollback, evidence collection, exception handling, and closeout.

• Define clear execution boundaries among orchestration services, CI/CD platforms, approval systems, source-of-truth platforms, AI advisory services, and automation execution engines.

API, Integration, and Data Engineering

• Design and implement RESTful, event-driven, streaming, and standards-based integrations among enterprise platforms, network infrastructure, source-of-truth systems, workflow engines, observability services, artifact repositories, and change-management systems.

• Define stable, versioned service contracts and data models that allow platform components to evolve independently while maintaining compatibility, security, and traceability.

• Build integrations using technologies and protocols such as REST, RESTCONF, NETCONF, gRPC, webhooks, message queues, event streams, OpenAPI specifications, and structured data formats.

• Develop data pipelines and services that collect, validate, normalize, correlate, and expose network state, software lifecycle data, workflow execution data, telemetry, release evidence, and operational outcomes.

• Establish patterns for data quality, lineage, ownership, freshness, access control, retention, reconciliation, and authoritative-source designation.

• Integrate network source-of-truth platforms such as Nautobot or NetBox with automation services, workflow orchestration, intended-state models, actual-state telemetry, compliance checks, and drift-detection processes.

Cloud-Native Engineering and CI/CD

• Design, build, and operate containerized services using Docker, Kubernetes, OpenShift, Helm, and comparable cloud-native technologies.

• Develop CI/CD and GitOps capabilities that automate build, testing, security validation, policy enforcement, artifact promotion, environment deployment, and release verification.

• Establish engineering patterns for promoting software and configuration safely across development, test, UAT, and production environments.

• Implement Infrastructure as Code and configuration automation using technologies such as Terraform, OpenTofu, Ansible, Helm, and Kubernetes manifests.

• Define standards for source control, branching, pull requests, protected branches, release tags, artifact integrity, dependency controls, and environment-specific configuration.

• Build automated test capabilities across unit, component, contract, integration, regression, performance, resiliency, and end-to-end testing.

Reliability, Observability, and Production Engineering

• Design highly available, fault-tolerant, scalable systems that can support mission-critical engineering and infrastructure workflows.

• Establish service-level indicators, service-level objectives, error budgets, availability targets, capacity expectations, and production-readiness requirements for platform services.

• Design observability architectures using metrics, logs, traces, events, flow data, streaming telemetry, and business-level workflow indicators.

• Implement dashboards, alerts, service health indicators, dependency views, and diagnostic capabilities using technologies such as OpenTelemetry, Prometheus, Grafana, ELK, Splunk, and distributed tracing platforms.

• Lead the development of automated detection, diagnostics, remediation, rollback, and evidence-capture patterns.

• Apply failure-mode analysis, resilience testing, controlled fault injection, capacity testing, and performance engineering to identify risks before production adoption.

• Ensure platform teams receive actionable health information rather than relying solely on infrastructure alerts or manual investigation.

AI-Enabled Software Engineering

• Design and develop governed AI-assisted engineering services that improve knowledge discovery, software development, test generation, release analysis, operational diagnostics, evidence summarization, and engineering decision support.

• Build AI application capabilities using retrieval-augmented generation, semantic search, structured outputs, tool integration, model evaluation, and human-in-the-loop approval patterns.

• Develop knowledge services that use approved and authoritative engineering sources, including standards, documentation, software reposito