We are seeking a Lead Data Architect to serve as a critical bridge between the EDO Enterprise Solution Architecture team and our business domains, with a strong focus on AI-aligned data initiatives. This role is embedded within a specific business function, serving as the domain鈥檚 trusted architectural authority to accelerate delivery of data mesh, data science, ML, and GenAI use cases. The primary objective is to translate enterprise architecture blueprints, platforms, and standards into tangible, high-value solutions, while providing continuous feedback to evolve those standards based on real-world applications.
This is a hands-on architectural leadership role requiring deep expertise in AWS and Databricks, modern cloud data architectures, and strong stakeholder influence and communication skills
Key Accountabilities
Architectural Enablement & Leadership
- Act as the primary Data architect and trusted advisor for the embedded business function, supporting initiatives from ideation through to production.
- Ensure enterprise Data, ML, and AI architecture principles are applied pragmatically to deliver measurable business value.
Solution Architecture & Design
- Partner with business stakeholders, product managers, and engineering teams to understand business use cases and constraints.
- Design and document end-to-end data, aligned with enterprise standards for:
o Data mesh and domain-oriented data products
o DataOps, MLOps, and emerging AgentOps practices
o AI-aligned data patterns (feature stores, RAG pipelines, inference architectures)
- Review and guide solution designs to ensure they are secure, scalable, resilient, and cost-effective.
Collaboration & Enterprise Alignment
- Work closely with domain leadership to help shape domain data and AI strategy and roadmaps.
- Act as the voice of the business domain within the central Enterprise Architecture and Data communities.
- Provide structured feedback to central teams to evolve enterprise blueprints, platforms, and governance models based on practical experience.
Governance, Risk & Best Practices
- Represent the business function in Central Design Authority and architectural review forums.
- Ensure solutions meet enterprise security, data governance, and AI risk standards while remaining delivery-oriented and scalable.
- Champion architectural best practices, data literacy, and AI-responsible design within the business function.
- Balance governance with enablement, ensuring standards accelerate rather than hinder delivery.
Strategic Guidance & Value Identification
- Proactively identify opportunities where data, ML, and AI initiatives can deliver significant business value.
- Advise on DataOps principles prioritisation and sequencing of initiatives based on feasibility, impact, and architectural readiness.
- Support the maturation of the domain鈥檚 data product and AI operating model.
Knowledge, Experience & Capabilities
- Deep knowledge of modern cloud data architectures on AWS, including lakehouse, streaming, and analytical workloads.
- Expert-level experience designing and operating Databricks Lakehouse platforms on AWS, with strong mastery of Delta Lake concepts (ACID transactions, schema evolution, time travel, and performance optimisation).
- Strong understanding of cloud security architectures, including VPC design, private connectivity, IAM least-privilege models, encryption, and enterprise identity integration.
- Solid grounding in data modelling paradigms (analytical, domain-oriented, event-driven) and their application in large-scale, multi-domain environments.
- Ability to translate enterprise blueprints into Domain-oriented Data Products, promoting decentralised ownership while maintaining central standards for interoperability and discoverability.
- Hands-on experience with enterprise metadata and governance tooling (e.g. Unity Catalog, Glue), including fine-grained access control and lineage
- Strong capability in infrastructure-as-code and environment standardisation to support scalable and repeatable delivery.
- Hands-on experience applying DataOps principles, including Data observability, CI/CD, testing, and operational resilience of data pipelines.
- Demonstrated ability to apply cloud cost management (FinOps) and the ability to influence cost-aware design decisions.
- Proven experience architecting data platforms that support ML and Generative AI, including feature stores, vector search, RAG pipelines, and inference architectures.
- 路 Exposure and deep appreciation for AI/BI, Ontologies and Knowledge Graphs
Critical success factors & key challenges
- Needs to be motivated, creative and curious, with a customer-centric mindset
- Able to engage with business and technical leaders with confidence and integrity
- A clear and effective communicator both at a team level and senior stakeholder level
- Able to manage ambiguity and ensure expectations are set appropriately
- The ability to balance a dynamic workload and prioritize effectively
- Comfortable working in a fast-paced environment and adapting to change
- Understand the main constraints and business objectives which our main stakeholder/business partners operate in