Cloud Engineer and Site Reliability; SME; Senior Manager

Position: Cloud Engineer and Site Reliability (SME) - Senior Manager
Job :
New York, NY, US, Salary: $172,800 - $359,000

Job Function:
Executive Management

Employer:

EY Global Services Apply by:
Company: EY

Location:

Anywhere in Country At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.  Join EY and help to build a better working world.

The opportunityAI-enabled services need cloud foundations that remain secure, observable, recoverable, and cost-aware as workloads change. You will serve as the hands-on technical authority for the engineering practices that move these services from experimentation into dependable production use.

This is a senior individual contributor role for someone who enjoys solving the problems other teams cannot. You will use AI-assisted engineering tools to accelerate infrastructure and automation work, apply your expertise to verify the results, and guide difficult decisions about architecture, performance, resilience, recovery, and cost across multiple use cases.

About Challenger Challenger is a new EY program focused on building the firm’s next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones.

Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale.

Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build.

Your key responsibilities

Build shared cloud foundations. Create reusable infrastructure, environment patterns, and deployment automation that help teams start quickly and operate consistently.

Use AI-assisted engineering responsibly. Apply AI tools to accelerate development and investigation while independently reviewing, debugging, testing, and validating the resulting code.

Guide complex designs. Review architectures, migrations, and releases where trade-offs affect reliability, security, performance, cost, or multiple use cases.

Strengthen production readiness. Define the evidence required before launch and identify what must change when a service is not ready to operate reliably.

Set reliability practices. Establish service objectives, observability standards, actionable alerts, and operational reviews that connect system behavior to user outcomes.

Make cost and capacity visible. Test workload assumptions and use performance, consumption, and demand evidence to guide scaling and architecture decisions.

Resolve difficult failures. Lead deep investigation across distributed services and integrations, verify root causes against system evidence, and test durable fixes.

Prove resilience. Design performance tests, failure simulations, recovery exercises, and rollback paths that show how services behave under stress.

Turn lessons into reusable capabilities. Publish tested reference implementations, guidance, and runbooks that state both the intended use and important limitations.

Raise engineering capability. Coach engineers, challenge assumptions constructively, and transition proven capabilities to ongoing owners with clear operating responsibilities.

Skills and attributes for success

Deep cloud expertise:
Strong knowledge of cloud infrastructure and distributed systems, with the ability to reason across system layers.

Hands-on engineering:
Ability to write, review, debug, and test infrastructure code, deployment automation, and production services.

Diagnostic…