Role Summary
We are looking for a well-rounded Principal Data Architect who can define the data architecture across a broad range of operational and analytical systems. This senior individual-contributor role will help shape several next-generation capabilities, including a Databricks-based lakehouse, an MDM platform supporting cross-domain product flows, an event-driven architecture built around Kafka, and related capabilities such as cross-domain authorization that depend on a consistent enterprise knowledge graph. The architect will set direction, make and document key trade-offs, and work closely with product managers, engineering teams and senior leaders to turn business needs into scalable, secure and cost-conscious data solutions.
Main Responsibilities
Define the target data architecture, system boundaries, principles and standards for operational, analytical and AI data use cases.
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Own conceptual, logical and physical data models, including canonical models, domain models, ontologies and relationships between domains, classes and properties.
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Design architectures covering OLTP, OLAP, lakehouse, event-driven, API, CDC, batch and streaming use cases.
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Select the right storage and processing patterns across row, column, key-value, wide-column, object and graph technologies.
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Define how data changes over time are handled, including versioning, history, slowly changing dimensions, Data Vault and event-sourced approaches.
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Define governance, metadata, lineage, quality, access-control, privacy, retention and auditability requirements.
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Establish data contracts, architecture decision records, reusable patterns and paved roads for delivery teams.
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Define enterprise data access patterns for AI, including semantic layers, vector and keyword retrieval, knowledge graphs, RAG and agent access to structured and unstructured data.
Required Skills and Experience
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Strong data modelling experience across business/conceptual, logical and physical models, including domain-driven design and knowledge representation.
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Strong understanding of distributed systems concepts, including CAP and PACELC, consistency, availability, latency and partition trade-offs.
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Practical knowledge of row stores, column stores, key-value stores, wide-column stores, object stores, lakehouse architecture and when to use each.
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Experience with Databricks or similar modern data platforms; strong understanding of lakehouse and data-product architecture.
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Experience with operational data modelling, not only data warehouse or lakehouse use cases.
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Strong event-driven architecture knowledge, including brokers, queues, event sourcing, claim-check pattern and delivery semantics.
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Knowledge of graph modelling and graph databases, including when graph relationships do not require a graph database.
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Understanding of security and privacy patterns including RBAC/ABAC, row/column security, encryption, classification, anonymisation and regulatory controls.
Nice to Have
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Experience defining canonical data models and governed data products across multiple product domains.
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Hands-on architectural experience with Databricks, Spark, Kafka or similar technologies and Azure or another major cloud platform.
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Experience in regulated, safety-critical or enterprise SaaS environments.
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