Engineering Manager – Platform Reliability

Architect and implement standardized, automated CI/CD pipelines across development, QA, staging, and production environments.

Drive reliable, repeatable, and near one-click deployments across multiple customer environments.

Design and implement Blue/Green and Canary deployment strategies to minimize downtime and release risks.

Enable independent deployment of application code, configurations, and database changes with safe rollback and recovery mechanisms.

Implement automated pre- and post-deployment validations, release versioning, artifact promotion, and deployment traceability.

Reduce deployment duration, manual intervention, and change failure rates.

Own Kubernetes-based platform architecture and operations across Microsoft Azure and AWS.

Drive infrastructure provisioning and deployment automation using Infrastructure as Code (IaC).

Establish GitOps-based management of Kubernetes manifests, Helm charts, and application configurations.

Design reusable infrastructure templates to support multiple customer environments.

Automate environment provisioning, including production-like environments using synthetic or de-identified healthcare data.

Optimize Kubernetes scheduling, autoscaling, capacity, availability, and cloud resource utilization.

Ensure proper separation of application artifacts, environment configurations, secrets, and persistent data.

Own production database reliability, particularly PostgreSQL, including backup, recovery, high availability, and disaster recovery.

Design automated schema migrations, database upgrades, and safe rollback and recovery procedures.

Implement database replication, synchronization, and Change Data Capture (CDC) mechanisms where required.

Establish Git-controlled reference data, seed data, and configuration management.

Automate database connectivity, integrity, and post-migration validation checks.

Define and regularly test Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO).

Partner with development teams to enable backward-compatible schema evolution and database backfills.

Establish Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error-budget practices.

Build end-to-end observability across microservices, infrastructure, databases, APIs, and distributed workflows.

Implement centralized logging, metrics, distributed tracing, dashboards, and actionable alerting.

Lead production incident management, root cause analysis (RCA), and corrective action tracking.

Develop automated operational runbooks, diagnostics, and recovery procedures.

Drive production-readiness reviews, performance testing, capacity planning, and operational risk assessments.

Continuously improve platform availability, Mean Time to Recovery (MTTR), and operational efficiency.

Own operational reliability of distributed systems involving Kubernetes, Kafka, Temporal, and asynchronous workers.

Design safe pause, drain, resume, and ownership-transfer mechanisms during application deployments.

Prevent duplicate or missed processing and unintended external side effects during system upgrades.

Monitor and recover scheduled jobs, background workers, and long-running workflows.

Partner with engineering teams to implement retries, idempotency, backpressure, rate limiting, and fault-tolerant recovery mechanisms.

Own DevSecOps practices across infrastructure, CI/CD pipelines, Kubernetes, and production environments.

Integrate vulnerability scanning, container image scanning, dependency scanning, and automated security validations.

Establish secure secrets and certificate management, least-privilege access, and production access controls.

Strengthen audit logging, network security, and infrastructure hardening.

Embed security controls into infrastructure provisioning and release automation.

Support HIPAA, HITRUST, and SOC 2 compliance through technical safeguards, operational controls, and audit evidence collection.

Coordinate vulnerability remediation and security incident response with security and compliance teams.

Ensure sensitive healthcare data is protected across development, testing, staging, and production environments.

Own cloud cost visibility, tracking, forecasting, and optimization across Azure/AWS infrastructure, Kubernetes workloads, databases, storage, networking, and observability.

Establish accurate cloud cost allocation and reporting by customer, environment, service, and engineering team.

Implement budgets, spend dashboards, cost anomaly detection, and proactive alerts for unexpected consumption.

Optimize resource utilization through rightsizing, autoscaling, capacity planning, storage lifecycle policies, and reserved or spot capacity.

Partner with engineering and finance teams to evaluate unit economics and cost-performance trade-offs.

Define and track FinOps KPIs, including budget variance, resource utilization, cost per customer/workload, and verified infrastructure savings without compromising reliability or security.

Build, mentor, and lead high-performing DevOps, SRE, and security engineering teams.

Provide hands-on technical leadership across architecture, automation, troubleshooting, and production incident management.

Define on-call coverage, escalation pathways, and operational ownership.

Collaborate closely with application, data, AI/ML, QA, and product engineering teams.

Establish a culture of automation, standardization, accountability, and continuous improvement.

Evaluate AI-assisted incident investigation, log analysis, automated root cause analysis, and operational diagnostics.

Prioritize reliability improvements and technical debt alongside product delivery.

Measure engineering effectiveness through DORA metrics and platform reliability indicators.

12+ years of experience in software engineering, infrastructure, DevOps, or Site Reliability Engineering, with proven technical leadership.

At least 4+ years of experience in technical leadership or engineering management.

Proven experience designing, building, and operating cloud-native production platforms.

Strong hands-on expertise in Kubernetes, Docker, Helm, and microservices-based architectures.

Ability to investigate complex production issues directly while mentoring and developing engineering teams.

Demonstrated experience leading production incidents, root cause analysis, and corrective actions.

Deep practical experience with Microsoft Azure, particularly AKS, networking, storage, identity, and managed databases.

Experience working with AWS cloud infrastructure.

Strong CI/CD experience using GitHub Actions, Jenkins, or equivalent tools.

Hands-on experience with GitOps, Infrastructure as Code (IaC), and environment automation.

Experience designing automated deployment pipelines and release management strategies.

Strong Linux administration, networking, and infrastructure troubleshooting skills.

Strong production experience with PostgreSQL, including backup, recovery, database migrations, replication, and high availability.

Understanding of database schema evolution, CDC, and zero-downtime migration strategies.

Experience operating distributed systems, asynchronous processing, and event-driven architectures.

Proficiency in Python, Go, Bash, or comparable automation languages.

Strong understanding of fault tolerance, retries, idempotency, and failure recovery.

Experience implementing SLIs, SLOs, error budgets, and reliability engineering practices.

Hands-on experience with monitoring, logging, distributed tracing, and incident management.

Experience implementing cloud security and CI/CD security controls.

Strong understanding of secrets management, access controls, vulnerability scanning, and infrastructure hardening.

Familiarity with HIPAA, HITRUST, SOC 2, or other regulated industry compliance requirements.

Experience managing and optimizing cloud infrastructure costs across Azure/AWS.

Understanding of cloud budgets, cost allocation, resource utilization, and infrastructure forecasting.

Experience implementing cost monitoring, anomaly detection, and optimization initiatives.

Ability to balance infrastructure cost efficiency with platform reliability, scalability, and security.

Cloud: Microsoft Azure (AKS preferred), AWS.

Containerization & Orchestration: Kubernetes, Docker, Helm.

CI/CD & GitOps: GitHub Actions, Jenkins, Argo CD, Flux.

Infrastructure as Code: Terraform, OpenTofu, or equivalent.

Databases: PostgreSQL, database replication, CDC.

Distributed Systems: Kafka, Temporal, event-driven architectures.

Observability: OpenTelemetry, Prometheus, Grafana, Azure Monitor, AWS Monitor.

Security: DevSecOps, vulnerability scanning, secrets management, infrastructure security.

Healthcare Interoperability: HL7, FHIR, clinical data processing workflows.

FinOps: Azure/AWS Cost Management, Kubernetes cost attribution, cloud cost optimization.

Hands-on engineering mindset with strong architectural and troubleshooting capabilities.

Strong ownership and bias for execution.

Comfortable operating in ambiguity and solving complex infrastructure and production challenges.

Ability to balance technical depth, people leadership, and business priorities.

Passion for automation, reliability, scalability, and operational excellence.

AI-first, AI-fast approach to engineering and operational problem-solving.

Outcome-driven mindset — focused on measurable improvements rather than operational activity.

Standardized and automated deployment workflows across customer environments.

Reliable, repeatable releases with minimal manual intervention and safe rollback mechanisms.

Highly available and resilient Kubernetes-based cloud infrastructure.

Improved database reliability, automated recovery, and tested disaster recovery processes.

Strong observability, incident response, and measurable improvements in MTTR and platform availability.

Secure production infrastructure aligned with healthcare compliance requirements.

Improved cloud cost visibility, resource utilization, and infrastructure efficiency.

Strong engineering teams capable of independently operating and continuously improving the platform.

Measurable improvements in DORA metrics, deployment frequency, change failure rate, and recovery time.

You have primarily managed teams without maintaining hands-on technical depth.

You view DevOps and SRE as infrastructure support functions rather than core engineering disciplines.

You are not comfortable troubleshooting complex production and distributed systems issues.

You lack experience with Kubernetes, cloud-native architectures, and automated deployment practices.

You prefer manual operational processes over automation and standardization.

You are not comfortable taking ownership of production reliability, security, and operational outcomes.

You are not excited about building and scaling AI-driven healthcare platforms.