Data Product Analyst

Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers. Accelerant was founded in 2018 by a group of longtime insurance industry executives and technology experts who shared a vision of rebuilding the way risk is exchanged – so that it works better, for everyone. The Accelerant risk exchange does business across more than 20 different countries and 250 specialty products, and we are proud that our insurers have been awarded an AM Best A- (Excellent) rating. For more information, please visit www.accelerant.ai.

The Data Product Analyst is a core contributor within a data mesh operating model, embedded in or closely aligned to business domains and accountable for the design, delivery, and evolution of insurance domain data products.

In 2026, this role will prioritize data products that enable improvements across Reserving, Reinsurance, and the Month Close process - including definition alignment, embedded reconciliation controls, and audit-ready lineage and documentation.

Acting as a domain “design authority,” the Data Product Analyst ensures data products are semantically correct, analytically fit for purpose, interoperable across domains, and compliant with relevant regulatory and reporting requirements.

Experience: Typically 6–10 years in data-aligned roles (e.g., data product, BI/analytics engineering, data governance/stewardship, domain analytics, technical BA/PO).

·       Translate domain needs across underwriting, pricing, claims, finance, and distribution into data product specifications with clear scope, assumptions, and acceptance criteria.

·       Define and maintain core insurance entities and relationships (e.g., policy, coverage, risk, claim, exposure, transaction), including grain and aggregation rules (e.g., policy-term, risk-level, claim-occurrence, transaction-level).

·       Define standard insurance metrics and logic (e.g., written/earned premium, loss ratio, frequency, severity, reserves) and ensure consistent interpretation across consuming teams.

·       Ensure the data product model accurately represents complex structures such as multi-line policies, endorsements, reinsurance structures, claims development, and reserving movements.

·       Design, enhance, and maintain data products in collaboration with data engineering and platform teams.

·       Oversee the full data product lifecycle, including versioning, enhancements, deprecation, and backward compatibility.

·       Support domain prioritization and roadmap planning for data products; maintain a transparent backlog aligned to business outcomes and OKRs.

·       Define and maintain the documentation to ensure certified data product are shipped with appropriate metadata including business definitions, lineage, quality expectations, usage guidance, ownership.

·       Collaborate with Data Quality and Data Governance teams to apply federated standards while preserving domain ownership.

·       Define and execute validation and testing strategies (quality thresholds, completeness checks, reconciliation validations) to ensure reliability for analytics and reporting consumers.

·       Perform impact analysis and coordinate change management for schema/metric/logic changes across consuming domains; communicate changes clearly and manage transition plans.

·       Act as the primary point of contact for data product consumers - supporting adoption, correct usage, interpretation, and self-service enablement.

·       Contribute to reusable templates/playbooks (metric definition template, acceptance criteria checklist, “definition of done” for data products) and coach domain SMEs in applying them consistently.

·       Data Product Specification: scope, entities, grain, metrics, assumptions, transformation/derivation logic

·       Data Product Contract: schema, SLAs, quality thresholds, lineage expectations, compatibility/versioning rules

·       Release Readiness Note: consumer communication, backward compatibility, cutover plan, monitoring expectations

·       Strong structured communication: concise, decision-oriented, able to drive alignment across Finance/Actuarial/Operations.

·       Ability to manage ambiguity, converge on definitions, and prevent “definition drift”.

·       Working knowledge of modern data platforms and concepts (pipelines, transformations, dimensional modelling/semantic layers).

·       Practical governance-by-design mindset (definitions, metadata, lineage, quality thresholds embedded into delivery).

·       Strong insurance domain knowledge covering policies/claims/premiums and downstream reporting usage.

Strongly preferred: experience with Reserving, Reinsurance, and/or Finance Month Close data and reporting processes.