Cloud & Data Engineering Manager: Lead Pipelines & AI

EY; Indianapolis (IN); Remote; USD 125,000 - 230,000; Full time

Benefits offered by this job

Medical and dental coverage

401(k) plans

Paid time off

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 workstreams, interact with clients, manage budgets, and lead teams across data engineering, software engineering, AI/ML, and cloud governance.

You will build scalable pipelines on Azure and other cloud platforms, apply Kubernetes-based architectures, and ensure production-grade delivery with observability and governance.

Qualifications

• 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).
• Experience designing, deploying and managing container orchestration platforms using Kubernetes.

Responsibilities

• Lead one or more technical workstreams and assure delivery quality.
• Translate business and client requirements into architecture and production platforms.
• Maintain daily client engagement and identify adjacent service opportunities.
• Own problems from raw source through data pipeline and production handoff.
• Design, provision and run the cloud and container platform used by application teams.

Skills

Azure expertise

Cloud engineering leadership

Agile delivery

Client engagement

Budget management

Education

Bachelor’s degree

Tools

Azure Functions

Azure Container Apps

Azure SQL Database

Azure Blob Storage

AKS

Azure Monitor

Terraform

GitHub Actions

Kubernetes

Prometheus

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 workstreams, interact with clients, manage budgets, and lead teams across data engineering, software engineering, AI/ML, and cloud governance.

You will build scalable pipelines on Azure and other cloud platforms, apply Kubernetes-based architectures, and ensure production-grade delivery with observability and governance.