About us

At Munich Re Specialty – Global Markets (MRS-GM), it is our ambition to become the leading Primary Specialty Insurance provider, underpinned by an effective and adaptable strategy, superior products and industry leaders working in a supportive environment to achieve this.

At the heart of our success is a strong culture where people are encouraged to be present, bold and curious, allowing them to achieve their individual goals.

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Head of Data Enablement

As Head of Data Enablement, you lead the supply side of the Central Data Office: the trusted, reusable data assets and standards that allow us to move faster and more safely. You own the shift from control-led data management to value-led enablement, making data accessible, trusted and fit for use, and embedding governance, quality and AI-readiness by design into the platforms and products MRS-GM and its AI depend on.

Success is measured not by the volume of controls or activity produced, but by whether teams across Munich Re Specialty pull your assets (certified data products, a shared semantic layer, safe self-service) because the sanctioned route is faster and more trustworthy than building their own tactical reporting.

You partner with Analytics, who embed analytics and use cases, and with the CIO function, who ingest data and build the platform. Your accountability is to turn raw, ingested data into certified products, and to make safe, governed, self-service use the default.

Objectives:

• Lead the move from traditional governance to value-led enablement that produces reusable, consumed assets — not controls for their own sake.
• Build and certify a catalogue of founding data products and the semantic layer beneath them, so priority use cases are served from trusted, shared data rather than duplicated, tactical reporting.
• Establish the data-product contract with engineering, so governance, quality and AI-readiness are applied by design to data ingested into the Global Markets strategic platforms.
• Make self-service safe and discoverable, reducing shadow reporting and time-to-trusted-data.
• Build a high-performing Enablement team, and ensure alignment with regulatory expectations where required.
• Measure and communicate the value of enablement in commercial terms aligned to Munich Re Specialty’s Ambition 2030 outcomes.

Responsibilities:

• Data products and catalogue: Define, build and certify reusable data products with clear ownership, consumers, SLAs, definitions and fitness-for-purpose. Stand up the founding catalogue e.g. (counterparty / entity, delegated authority, finance, and portfolio / exposure) with a one-page specification per product that the team can use without you in the room. Deliver an early, visible asset (e.g. the semantic layer for the entity product) within the first quarter.
• Semantic layer and shared definitions: Own the semantic layer, data dictionary and business glossary, so there is one trusted definition of each core entity and measure. This is what enables cross-functional understanding, removes argument over numbers, and makes self-service safe.
• The data-product contract with engineering: Partner with the CIO’s engineering function on a data contract at the source: engineering delivers ingested data to an agreed interface; Enablement curates it into certified products; upstream changes cannot silently break downstream consumers. Ensure governance and quality are applied up front to data entering the strategic platforms and operational systems. This closes the ingestion gap behind shadow reporting.
• Proportionate governance by design: Encode policy into technical controls embedded in workflows and platforms, rather than manual review after the fact. Keep controls proportionate to business risk, and rationalise them across the other compliance data functions for interoperability and consistency. Pair the role with the authority and decision rights needed to make standards stick.
• Data quality: Define quality as fit for the critical process it serves, not as standalone metrics. Embed quality ownership at source and in systems, and prioritise remediation by business and regulatory impact.
• AI-readiness and AI governance: Make priority data usable, discoverable and fit for AI, and own AI governance: responsible-AI controls, model-data lineage and readiness, and alignment with emerging regulation.
• Self-service, discoverability and modern platforms: Make trusted data discoverable (catalogues), accessible (self-service) and reliable (fit-for-purpose pipelines) on the modern platform, so the sanctioned route out-competes tactical builds. Drive adoption of these platforms for real, prioritised use cases rather than for their own sake.
• Change enablement: Ensure change initiatives consume certified products and complete a data-product requirement at scoping. Supply the standards, specifications and quality gates that make programmes genuinely data-led. Embedded delivery within programmes is owned jointly with Product & Delivery; your accountability is that the reusable supply exists, is trusted, and is used.