Site Reliability Engineering Manager

About the Team

SRE Middleware & Data (SRE M&D) enables product teams by providing secure, scalable, and automated data, middleware, and AI services on Azure.

Provisioning: Automates secure delivery of databases, queues, caches, and AI services/resources; builds self-service and guardrails.

Reliability: Ensures availability, performance, and resilience of data, middleware, and AI services in production; builds observability, autohealing, and disaster recovery.

What You Will Do

• Lead and grow the team: Manage, mentor, and develop SRE engineers across Provisioning and Reliability.

• Lead cross-team initiatives:

• Align roadmaps with Architects and partner teams; ensure adoption of architecture standards.

• Run design reviews and architecture signoffs; surface and mitigate risks and complexity early.

• Translate standards into guardrails and automation (policyascode, selfservice) for consistent delivery.

• Apply lightweight RACI and clear escalation paths to resolve tradeoffs quickly.

Drive roadmap and execution:

• Provisioning: Self-service engines, Crossplane/Terraform-based automation, policy-as-code, secured pipelines, access management, backups/restore, Azure AI resource provisioning, and quota management.

• Reliability: Observability integrations (New Relic, Azure Monitor), performance tuning, autohealing, DR/BCP, resilience testing, failover automation, SLOs, reliability for Azure AI services and messaging platforms (Kafka, Event Hubs, Service Bus).

Establish engineering excellence:

• Infrastructure as Code (Terraform/Crossplane) and CI/CD best practices.

• Change management with safe deploys and rollback strategies.

• Incident and problem management with blameless postmortems.

• Continuous improvement loops measured by DORA/SRE metrics.

• Champion security and compliance by design:

• Policy as code, least privilege access, and secrets/identity hygiene.

• Guardrails in self-service flows; auditability and evidence collection.

• Partnership with Security/Compliance for standards and reviews.

Partner with the Architects:

• Codevelop platform architecture and service standards.

• Define SLIs/SLOs and capacity/reliability patterns for core services.

• Align roadmaps and run design reviews for high-impact changes.

Own delivery outcomes:

• Navigate competing priorities across a broad platform scope — balancing reactive operational load (incidents, toil, on-call) against proactive platform investment (self-service, automation, resilience) without a clean separation between the two.

• Make and communicate prioritization decisions under ambiguity, with partial information, across teams that have conflicting urgency.

• Maintain a defensible, visible backlog that reflects real risk and business impact — not just the loudest stakeholder.

Operate a healthy on-call:

• High-quality playbooks and automation-first troubleshooting (AI-assisted).

• Actionable alerts with SLO-based paging and noise reduction.

• Regular resilience testing and post-incident hardening.

Initiatives You Will Lead:

• Self-service provisioning for databases, queues, and caches with golden configurations and policy guardrails.

• AI-assisted troubleshooting for provisioning and production incidents.

• Platform wide observability integration for data, middleware, and AI services (New Relic, Azure Monitor).

• Automated DR runbooks and resilience/chaos testing in production.

• Performance tuning at service and query layers, including automated tuning workflows.

• Standardization of provisioning via Terraform/Crossplane for databases, messaging, and AI services.

• Governance for Azure AI services (quotas, access, safety guardrails) with clear consumption patterns for product teams.

Success Metrics

• Reduced MTTR and incident count, improved SLO attainment for data/middleware services.

• Improved lead time for change and change failure rate; increased automation coverage and reduced toil.

• Faster time to provision and higher first success rate for self-service requests.

• Measurable improvements in cost efficiency, performance, and capacity predictability.

• Team health: engagement, growth, hiring velocity, stress levels, and retention.

• SLO attainment for AI endpoints and messaging services; reduced alert noise via improved observability (New Relic/Azure Monitor).

What You Will Bring

• Proven experience managing SRE/platform/infrastructure teams delivering production-critical services.

• Deep familiarity with Azure and the team stack: PostgreSQL, MongoDB, Cosmos DB; Redis; messaging systems such as CloudAMQP/RabbitMQ, Kafka, Event Hubs, and Service Bus.

• Strong reliability fundamentals: SLOs/SLIs, incident and problem management, capacity, DR/BCP, performance tuning.

• Solid automation background: IaC (Terraform/Crossplane), CI/CD (Azure DevOps), GitOps, policyascode, secrets and identity, RBAC.

• Track record of building selfservice platforms and reducing toil.

• Excellent crossfunctional leadership with product, security, and compliance partners.

• Experience operating Azure AI services in production (Azure OpenAI, Cognitive Services, AI Search).

• Observability experience with New Relic, Azure Monitor, and OpenTelemetry.

What You’ll Need

• Advanced skills in English

• Experience with AI-assisted operations and troubleshooting.

• Observability expertise (Prometheus/Grafana, New Relic, Azure Monitor, OpenTelemetry).

• Database performance engineering and query optimization.

• Experience in regulated environments and security frameworks.

• FinOps capabilities (cost governance, forecasting, rightsizing, quotas, budgets, chargeback/showback).

How We Work

• Collaboration first with a strong partnership between the Engineering Manager and the Architects.

• Automation by default; security and reliability are nonnegotiable.

• Blameless postmortems, continuous learning, and measurable outcomes.

• Participation in an equitable on-call rotation with high-quality runbooks and automation.

Tech Environment

Azure (AKS, managed databases, storage, networking, identity).

Azure AI services (Azure OpenAI, Cognitive Services, AI Search).

Azure DevOps for CI/CD.

CloudAMQP (RabbitMQ).

Databases: PostgreSQL, MongoDB, Cosmos DB.

Caches: Redis.

Queues/Brokers: Kafka, Event Hubs, Service Bus.

Terraform/Crossplane, GitOps.

Observability: New Relic, Azure Monitor, logs, metrics, traces, alerting workflows.