Cloud & Data Engineer - Manager - Consulting - Location OPEN

EY•Hartford, DC

2d•$125,500 - $261,600•Remote

About The Position

For a Data and Cloud Engineering-aligned Manager, the purpose is to turn data and AI capabilities into scalable, reliable and governable production solutions. This Data and Cloud Engineer Manager role spans the full platform and data asset lifecycle, from assessment and architecture through ingestion, transformation, governance, consumption, monitoring and support. The client-serving Data and Cloud Engineering Manager explicitly owns workstream delivery, quality, client interactions, opportunity identification, budgets and engagement economics. A person successful in this role will demonstrate and support team leadership, technical-talent development and commercial acumen.

Requirements

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 but not limited to: Azure Functions, Azure Container Apps (or Azure App Service), Azure SQL Database, Azure Blob Storage, Azure Kubernetes Service (AKS), Azure Monitor (Log Analytics & Application Insights), 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

Nice To Haves

A passion for delivering commercial software products and platforms to market
Be deeply technical and constantly in a state of learning
Able to communicate clearly and efficiently with a variety of audiences including developers, clients, customers, partners and executives
Fearless in getting “hands-on” with technology and execution
Strong understanding of modern software engineering processes
Deep understanding and interest in cloud and distributed systems architectures
Comfortable with ambiguity with a drive for clarity
Experience leading and growing technical talent
Experience in leading teams and managing change effectively
Strong negotiation and influencing skills
A proven track record of building and managing client relationships
Strong negotiation and influencing skills
Experience in managing change effectively
A track record of leading teams successfully.

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 (e.g. spanning data engineering, software engineering, AI/ML, architecture, cloud and governance).
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