Remote Cloud & Data Engineering Manager

EY; Rogers (AR); Remote; USD 126,000 - 230,000; Full time

Job summary

EY is seeking a Data and Cloud Engineering Manager to turn data and AI capabilities into scalable, reliable production solutions. You will own the full platform lifecycle from assessment through production handoff, leading technical workstreams and engaging with clients to identify opportunities.

The role requires hands-on cloud engineering with a strong focus on Azure, Kubernetes, Terraform and modern data platforms, plus experience in governance, performance, and cost optimization.

Qualifications

• A bachelor’s degree required (4-year degree).
• Typically, no less than 4–6 years of relevant experience.
• Proven record operating High Availability services on Azure (4+ years).
• Expert understanding of core Azure services such as: Azure Functions, Azure Container Apps, Azure SQL Database, Azure Blob Storage, AKS, Azure Monitor, and Azure Managed Prometheus.
• Experience designing, deploying and managing container orchestration platforms using Kubernetes.
• Experience implementing SRE standards for resiliency and scalability of microservice architectures in Kubernetes.
• Experience with infrastructure automation using Terraform.
• Experience implementing SLIs, SLOs and Error Budgets as part of development/delivery practices.
• Experience monitoring container based microservices & Cloud Platform services.
• Use of CI/CD tools - preferably GitHub Actions, Azure DevOps.
• Knowing agile methodology and being able to work by its principles.
• Strong analytical and problem-solving skills.
• Self-starter and strong, quick problem-solving skills.

Responsibilities

• Lead one or more technical workstreams and assure delivery quality.
• Translate business and client requirements into architecture, pipelines, models, semantic layers or production platforms.
• Maintain daily client engagement, participate in working sessions and identify adjacent service opportunities.
• Manage resource plans, budgets and engagement economics.
• Own problems from raw source through data pipeline, model/analysis and production handoff.
• Automate, optimize and drive efficiency in processes, tools and communication across cross-functional teams.
• Remain hands-on: tested, version-controlled, observable code rather than one-off analytical notebooks.
• Work closely with development teams and participate in planning sessions to achieve high level of availability, reliability and performance of their service and platform requirements.
• Design, provision, and run the underlying cloud and container platform that application teams rely on in production.
• Build scalable pipelines and streaming architectures using Python/SQL and cloud platforms (Azure predominantly, AWS and GCP also key platforms).
• Understand and apply data architecture, modelling, integration, quality, security, ontologies and semantic layers.
• Understand and apply quality assurance and testing practices.
• Understand and apply cloud and distributed-system architecture and the path to production using Kubernetes, GPU infrastructure, multi-tenancy, infrastructure-as-code, GitOps, resilience and hybrid/multi-environment portability.
• Incorporate GenAI/LLM capabilities where relevant.
• Demonstrate proficiency in Agile delivery and development methodologies.
• Embed governance, observability, controls and operational support—not stop at model development or analysis.

Skills

Azure expert

Kubernetes

GitHub Actions

Terraform

SRE practices

Agile methodologies

Cloud architecture

Data engineering basics

Education

Bachelor's degree

Tools

Azure Functions

Azure Container Apps

AKS

Azure SQL Database

Azure Monitor

Prometheus

Terraform

GitHub Actions

Azure DevOps

Job description

EY is seeking a Data and Cloud Engineering Manager to turn data and AI capabilities into scalable, reliable production solutions. You will own the full platform lifecycle from assessment through production handoff, leading technical workstreams and engaging with clients to identify opportunities.

The role requires hands-on cloud engineering with a strong focus on Azure, Kubernetes, Terraform and modern data platforms, plus experience in governance, performance, and cost optimization.