Head of Data & Context Engineering

About the role

Manulife Japan is rebuilding its data foundations from scratch to create one trusted context of customers, distributors and business, shared across employees and AI agents. The Head of Data & Context Engineering builds an in-house data and context engineering capability, runs the data platform as a product and embeds data governance into it. The role is hands-on, partnering with senior leadership, the Technology department and regional and global Data & AI teams. The working arrangement is hybrid.

Key responsibilities

• Build and lead an in-house data and context engineering team and culture, hiring, growing and setting engineering standards.
• Own the data platform as a product: self-service data, a catalog and access that takes minutes once users are trained.
• Own data governance and build it into the platform: access, lineage, quality and policy enforced automatically, working with data stewards on day-to-day stewardship.
• Run DataOps: CI/CD, test automation and infrastructure as code, to release small changes often and safely.
• With the AI team, build the context layer for AI agents: a semantic layer, core metrics defined once and metadata captured as work happens, using AI to automate data engineering and governance.
• Engage senior leadership on data strategy, steer data governance with business data owners, and work with Technology and regional and global teams on architecture, standards and platform direction.

Required profile

• 10+ years in data engineering or data platforms, including 5+ years leading engineering teams.
• Proven track record of building an in-house data engineering team and culture.
• Experience running an enterprise cloud data platform as a product (Azure, Databricks or equivalent).
• Ability to influence senior business and technology leaders.
• Business-level English; business-level Japanese preferred.

Required skills

• Strong CI/CD, test automation and infrastructure-as-code practice applied to data.
• Experience building governance into a platform (access, lineage, data quality, policy as code).
• Cutting-edge data architecture skills, including data for AI (semantic, metadata and ontology layers, context for AI agents).
• Preferred: agile operating models (squads, chapters, product ownership), GenAI and AI agent tooling, self-service platforms for citizen developers, data transformation in complex or legacy environments, and experience in insurance, financial services or another regulated industry.