Enterprise Data Architect

Aztec Group

Enterprise Data Architect responsible for designing Azure Data Platform architecture at Aztec Group. Collaborating with teams to implement data solutions and improve data governance.

Posted 7/17/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in defining and maintaining enterprise data architecture, with a focus on Databricks Lakehouse platform management, data governance, and compliance with security and regulatory standards. Proficient in data modeling, performance optimization, and integration patterns for external data sharing.

Highest-signal resume keywords

Databricks ProficiencyMaster Data Management DesignData Governance FrameworksData Modeling SkillsSQL and Python Knowledge

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

DatabricksUnity CatalogDelta LakeMaster Data ManagementData ModelingSQLPythonData GovernanceData SecurityPerformance Optimization

Soft Skills

Excellent Communication

Tools & Technologies

Microsoft PurviewAPIsEvent StreamingManaged File TransferData Quality Management

Industry Keywords

GDPRData ResidencyData StewardshipData LineageData Cataloguing

Tech Stack

Tools & technologies

AzurePythonSparkSQLUnityVault

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Discretionary bonus scheme
Flexible, hybrid working
Ability to work abroad for up to 3 weeks per annum
Generous holiday allowance
Pension scheme
Private medical insurance, including eye care
Life assurance (death in service and critical illness benefit)
Worldwide travel insurance
Health and wellbeing programmes
On-site parking (location dependent)