Cloud & Data Engineer - Manager - Consulting - Location OPEN

About the role

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.

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