Lead Data Designer

Individually we are people, but together we are Aviva. Individually these are just words, but together they are our Values – Care, Commitment, Community, and Confidence.

At Aviva Canada , we put people first, our employees, our customers, and our communities. We’re proud of a culture built on care, inclusion, and collaboration, where your voice matters and your growth is supported. We’re not just about insurance; we’re about making a real difference by protecting what matters most.

At Aviva, data is at the heart of every customer experience, business decision, and innovation journey. As a Lead Data Designer, you'll play a critical role in shaping our enterprise data landscape by defining the standards, models, and frameworks that enable trusted, secure, and reusable data across analytics, AI, and digital solutions. Working closely with business and technology partners, you'll lead end-to-end data design initiatives, apply Property & Casualty insurance expertise and industry standards such as ACORD, and help deliver scalable data solutions that support digital transformation, platform modernization, and future growth. You'll also influence how Canadian data domains align with Aviva's global data strategy while ensuring local business needs remain at the forefront.

What you'll do

The Lead Data Designer establishes clear, consistent, and implementation-ready data designs across the enterprise. Key responsibilities include:

• •Enterprise domain authority: Own and govern data models for assigned domains, aligning them with business capabilities, target architecture, roadmaps, and Aviva Canada’s P&C ecosystem. Resolve semantic conflicts and manage shared identifiers, classifications, reference data, hierarchies, and golden records where applicable.
• •Business semantics and federated ownership: Partner with business experts, product managers, and data owners to define and drive adoption of key business concepts, critical data elements, domain boundaries, systems of record, and producer/consumer responsibilities. Ensure data models, glossaries, and contracts enable effective business, engineering, governance, and AI outcomes.
• •Solution and integration modeling: Lead data design across operational stores, warehouses, data lakes/lakehouses, data products, and relational or NoSQL platforms. Apply fit-for-purpose modeling approaches and use approved logical models to support integrations, APIs, events, analytics, data science, and emerging AI consumption patterns.
• •Standards, contracts, and semantic governance: Govern data modeling standards, naming conventions, and design patterns. Define and maintain data contracts covering ownership, schemas, quality, service levels, versioning, and compliance, with automated validation where appropriate. Ensure metadata, lineage, business glossaries, transformation rules, and governance controls support consistent data use across business, technology, and AI solutions.
• •Migration and physical optimization: Define canonical structures, mappings, history, survivorship, validation, cutover, and transition-state designs for migration and modernization. Optimize physical models for performance, scalability, maintainability, cost, and workload patterns using partitioning, clustering, indexing, denormalization, and query optimization where appropriate.
• •Privacy, security, and regulatory controls: Embed privacy and security by design, least privilege, classification, retention, masking, tokenization, and access controls. Ensure models support Canadian insurance reporting, auditability, records and evidence requirements, data residency and cross-border obligations, and insurance risk, actuarial, pricing, and capital modeling.
• •AI-ready data: Design governed datasets, semantic layers, feature models, and retrieval-ready content to support AI use cases. Ensure data is trustworthy, compliant, and fit for purpose by addressing quality, lineage, provenance, privacy, security, and bias considerations. Establish clear classification and governance for structured, unstructured, and multimodal data to improve AI accuracy, traceability, and trust.
• •Design assurance and conformance: Review and approve data models, schemas, APIs, and events based on established principles, risks, and trade-offs. Assess downstream impacts across data products, integrations, analytics, regulatory reporting, AI solutions, and consuming systems. Ensure implementation aligns with approved standards, document exceptions, and maintain clear decision records.
• •Data Views and governance: Recognize that Data View identifies the data domains/subdomains affected and their alignment with enterprise models, standards, ownership, and governance. Over time, assume ownership of the enterprise Data View and the Solution Data View for all projects, working through architecture forums, data governance boards, stewardship councils, and roadmap planning to balance enterprise control, regulatory obligations, reuse, and federated business agility.
• •Model lifecycle and observability: Manage model ownership, versioning, traceability, publication, discoverability, effective dating, change control, supersession, and retirement. After implementation, monitor adoption, semantic consistency, anomalies, schema drift, metadata and lineage completeness, data quality, contract compliance, compatibility, realized change impacts, and approved exceptions. Prioritize corrective action and continuous improvement with data owners, data-product owners, platform teams, and delivery backlogs.
• •Delivery leadership: Guide and coach data, business intelligence, analytics, AI, application, architecture, engineering, delivery, and external partner teams. Balance target-state design with delivery needs, legacy constraints, and incremental modernization.

What you'll bring

• •Experience and education: 10+ years of enterprise data modeling and design experience in large, complex organizations, including leadership of large-scale or cross-domain models. Bachelor’s degree in computer science, Information Systems, Data Management, Engineering, or related discipline, or equivalent practical experience.
• •Enterprise modeling and governance: Expertise in conceptual, logical, and physical modeling; normalization; dimensional modeling; data vault; semantic layers; glossaries; ontologies; knowledge and context graphs; active metadata; machine-verifiable data contracts; and structured and semi-structured schemas for human, analytical, application, and AI consumers. Strong knowledge of data governance, quality, metadata, master and reference data, stewardship, lineage, retention, privacy, and security.
• •Data engineering and integration: Strong SQL, data profiling, source-system and query analysis, reconciliation, and source-to-target mapping skills. Strong knowledge of ETL and ELT, batch and streaming integration, APIs, change data capture, event-driven data, and versioned API and event contracts, including ownership, backward compatibility, and schema evolution.
• •Platforms and modeling tools: Experience with cloud data platforms and modern data ecosystems. Knowledge of Snowflake, AWS data services, PostgreSQL, Hadoop, Informatica, dbt, Airflow, AWS Glue, SAP PowerDesigner, ERwin Data Modeler, or comparable modeling, metadata, and lineage tools is an asset.
• •Data delivery: Experience applying enterprise semantic, metadata, quality, lineage, privacy, and security practices to governed data for machine learning, generative AI, retrieval-augmented generation, agentic AI, feature engineering, and data products, including vector and embedding structures, representativeness, bias risk, and permitted-use controls.
• •P&C and industry standards: Deep knowledge of Property & Casualty insurance concepts, lifecycle, terminology, and data relationships across personal and commercial lines. Ability to translate them into stable, reusable models, apply ACORD concepts, and align Canadian models with global or federate