Enterprise Data Architect

Aztec Group

1d•Hybrid

About The Position

At Aztec, we’re committed to building and nurturing a diverse and inclusive workforce where everyone feels valued, respected, and able to grow. We know that different backgrounds, perspectives, and experiences strengthen our business and we welcome applications from all individuals. So, if your experience doesn’t exactly match with every part of the job description, but you are excited about the role, we would still like to hear from you. If you are passionate, curious, innovative and data driven, you could still be a good fit. Reports to Head of Enterprise Architecture At the Aztec Group we credit our technology as one of the core ingredients to our award-winning outsourced solutions. We are currently looking for an Enterprise Data Architect to join our architecture team and help Aztec to deliver their exciting new technology strategy, leveraging Databricks. This role will centre on the design, implementation and maintenance of data models and related architecture for our Azure Data Platform. You will provide technical expertise Under the direction of an experienced Manager and with support for continuing professional development, the ideal candidate will become a key part of the Group’s Architecture team.

Requirements

Deep, hands-on proficiency with Databricks, including Unity Catalog, Delta Lake, workflows, and platform administration concepts.
Practical experience with Databricks Delta Sharing and designing integration patterns for external data sharing (e.g. Delta Sharing, secure APIs, event streaming, managed file transfer), including the security and contractual considerations involved.
Strong track record designing and implementing Master Data Management solutions, including tooling selection, data stewardship models, and reference data management.
Demonstrable expertise in Databricks performance and cost optimisation at scale.
Strong data modelling skills (dimensional, 3NF, Data Vault or similar) and experience defining enterprise data models.
Solid grounding in Azure, including storage, networking, and identity concepts relevant to data platforms.
Experience with data governance frameworks and tools (e.g. Microsoft Purview), data quality management, metadata, and lineage tooling.
Strong SQL and working knowledge of Python and/or Spark.
Understanding of data security and privacy: encryption, access control (RBAC/ABAC), data masking, and regulatory compliance (GDPR).
Excellent communication skills, with the ability to explain architectural decisions in plain language to executive and non-technical audiences.
Quick to learn new systems and business processes and great with people, as close working relationships between our colleagues and clients is at the heart of what we do.

Nice To Haves

We will provide the training, both in house for relevant technical knowledge and also professional qualifications to enhance your professional development.

Responsibilities

Define and maintain the enterprise data architecture, including target-state designs, roadmaps, and architectural standards and patterns.
Own the architecture of our Databricks Lakehouse platform, including workspace design, Unity Catalog structure, medallion architecture, and environment strategy.
Design and govern integration patterns for sharing data with external clients and third parties, including secure, scalable approaches using Databricks Delta Sharing, APIs, and file-based exchange where appropriate.
Lead the design and adoption of Master Data Management (MDM), including entity definitions, golden-record strategy, match-and-merge rules, and stewardship processes.
Establish standards and practices for optimising Databricks performance and cost — cluster and compute strategy, Delta table, caching, and workload right-sizing.
Define data modelling standards (conceptual, logical, physical) across analytical and operational domains.
Work with data governance colleagues to embed data quality, lineage, cataloguing, classification, and access-control policies into platform design.
Ensure architectures meet security, privacy, and regulatory requirements, including GDPR and data residency considerations.
Review solution designs, provide architectural assurance, and support engineering teams through design authority and peer review.
Design data architectures that enable AI adoption — ensuring data is discoverable, well-governed, and AI-ready for machine learning, GenAI, and agentic use cases across the business.
Partner with AI/ML teams to define patterns for feature engineering, model data pipelines, vector search, and the safe exposure of enterprise data to AI systems.
Evaluate emerging technologies and make pragmatic build/buy/adopt recommendations, including data and AI enablement opportunities.