Manager Engineering Strategy

Description

The team is the Engineering, AI & Data practice, and this role is a manager position in it.

The role is about growing the practice commercially while leading transformation work for clients in engineering, AI and data.

The company is a professional services firm working on enterprise technology transformation.

Responsibilities

• Drive commercial and strategic growth of the AI and data offerings.
• Spot opportunities, shape them into proposals, and turn them into engagements that make an impact.
• Open the door for technology experts and software engineers to start engagements on platform and product strategy, design and architecture.
• Lead transformation programmes covering enterprise data platforms, AI, cloud and emerging technologies.
• Help clients build engineering, AI and data blueprints and roadmaps.
• Own projects from early vision through implementation and adoption.
• Inspire, coach and grow multidisciplinary project teams.
• Build a learning environment that offers ongoing growth.
• Work with talent teams to hire, develop and keep strong people.
• Promote a high-performance culture built on collaboration and integrity.

Requirements

• At least 7 years as a software engineer and solution architect on complex, enterprise-grade systems.
• Past project experience as a Product Owner, Product Manager, Scrum Master or Tech Lead.
• A proven record of leading large-scale engineering, cloud, AI and data transformation programmes from start to finish.
• Extensive experience advising senior executives as a strategic partner.
• A real passion for technology, with broad knowledge and deep skills in key areas.
• You can explain complex technical topics as clear, usable insights.
• In-depth experience with a range of technology stacks: cloud platforms (AWS, Azure, GCP) and cloud-native designs such as microservices, serverless and Kubernetes.
• Experience with data and AI platforms such as Databricks and Snowflake, real-time streaming tools (for example Kafka and Airflow) and MLOps.
• Experience with generative AI and advanced analytics, including LLMs, prompt engineering, AI orchestration and responsible AI.
• Experience with integration and APIs, including event-driven architecture and API management.
• Experience with DevOps and platform engineering, including CI/CD, infrastructure as code and observability.
• Experience with on-premise infrastructure and open source tools.
• Networking knowledge, such as CCST, CCNA, CCNP or CCIE.
• A strong commercial mindset: you spot opportunities, shape value propositions and build long-term client relationships.
• Strong communication and leadership skills that inspire teams and bring diverse stakeholders into line.
• A proven ability to work through complex stakeholder environments spanning business and IT.
• A self-starter who can build a business.