← Role Archetypes
Data Specialist Track

Data Architect

SFIA 6-7
GDE → JDE → IMDE → SDE →
TTL → EM
LSE → DA
→ HoE → VP

Defining enterprise data architecture, shaping data governance and lineage strategy, setting technology direction, and ensuring the organisation's data estate is structured to deliver lasting business value.

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Growing into this role

Senior Data Engineer to Data Architect

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Performance Calibration

Data Architect - Performance Levels

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Behaviour Tracker

Data Architect - Performance Tracker

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Overview

As a Data Architect, you define the structural foundations of the organisation's data estate. You set the enterprise-level patterns for how data is modelled, governed, stored, and served - ensuring that the organisation's investment in data creates compounding value rather than compounding complexity. Your decisions shape the work of data engineering teams across the business.

You operate at the intersection of technology strategy, business strategy, and engineering craft. You are expected to hold a clear, well-reasoned long-term view of the organisation's data architecture, communicate it authoritatively, drive adoption across teams, and continually evolve it as technology and business needs change.

Key Responsibilities

Enterprise Data Architecture

  • Define and maintain the organisation's enterprise data architecture - the structural patterns, standards, and principles that govern how data is modelled, integrated, stored, and served across the business.
  • Develop and communicate the target state data architecture - where the organisation is heading, how it will get there, and what trade-offs are involved.
  • Review and govern significant architectural decisions made by data engineering teams - ensuring coherence with enterprise patterns and flagging deviations that create systemic risk.
  • Lead the design of critical, cross-cutting data infrastructure - master data management, data integration patterns, canonical data models, and enterprise taxonomies.
  • Balance long-term architectural ideals with the practical constraints of delivery pace, team capability, and legacy systems.

Data Governance and Lineage

  • Define and drive the organisation's data governance framework - data ownership, stewardship, quality standards, lineage tracking, and cataloguing.
  • Establish data modelling standards and conventions that apply across teams - ensuring data assets are consistently structured, named, and documented.
  • Lead the organisation's approach to data lineage - ensuring it is possible to trace data from source to consumption for both operational and regulatory purposes.
  • Work with data, compliance, and legal stakeholders to ensure the data architecture meets regulatory and privacy requirements.
  • Chair or contribute to data governance forums - driving accountability for data quality and ownership across the business.

Technology Strategy and Selection

  • Define the organisation's data technology strategy - which platforms, tools, and patterns to adopt, standardise, and retire.
  • Evaluate emerging technologies and architectural patterns - assessing their maturity, fit, and cost against the organisation's needs.
  • Build and maintain the data tooling radar - providing clear guidance to engineering teams on what to use, what to experiment with, and what to avoid.
  • Lead vendor assessments and technology selection processes for significant data platform investments.
  • Maintain awareness of the total cost of ownership of the data technology estate and advise on investment decisions.

Organisational Capability and Influence

  • Build data architectural capability within the engineering organisation - coaching senior data engineers, developing architecture review processes, and growing the next generation of data architects.
  • Represent data architecture in executive and senior leadership forums - translating complex architectural concerns into business risk and opportunity language.
  • Collaborate with engineering, product, and business leadership to ensure data architecture investments are aligned with strategic business goals.
  • Build a community of practice around data architecture - connecting practitioners across teams and driving shared standards.
Role Specific

Enterprise Data Modelling Standards

Define and govern the data modelling standards, canonical models, and naming conventions used across the organisation's data estate - ensuring that data assets are interoperable, interpretable, and fit for long-term use.

Data Governance and Compliance Architecture

Design the governance frameworks, lineage systems, and access control architectures that ensure data is used appropriately, traceably, and in compliance with regulatory requirements.

Data Technology Strategy

Own the organisation's data technology radar and platform strategy - making informed, evidence-based recommendations on technology adoption, standardisation, and retirement that balance capability, cost, and risk.

Behaviours

Learning & Growth

  • Maintains deep awareness of the evolution of data architecture - tracking emerging patterns in data mesh, data contracts, semantic layers, and real-time data at scale.
  • Actively builds a peer network of data architects across the industry - learning from others solving similar problems at similar scale.
  • Reads foundational and cutting-edge literature on data architecture, data governance, and information management.
  • Reflects critically on past architectural decisions - publishing internal post-mortems that help the organisation learn from both successes and failures.
  • Develops breadth across adjacent disciplines - AI/ML architecture, application architecture, security - to design data architectures that integrate cleanly with the wider technical estate.
  • Continuously reassesses existing architectural commitments in light of new evidence - willing to evolve direction when circumstances change.

Delivery

  • Delivers architectural artefacts - target state diagrams, ADRs, standards documents, technology radars - with the same rigour and timeliness expected of engineering delivery.
  • Maintains a visible, actionable roadmap for the data architecture evolution - not just a vision but a sequenced programme of work with clear dependencies.
  • Balances the tension between architectural idealism and delivery pragmatism - making explicit, documented decisions about where to accept short-term debt.
  • Drives adoption of architectural standards through enablement, review, and feedback - ensuring architecture is implemented as intended, not just documented.
  • Coordinates across multiple engineering teams on cross-cutting architectural work - maintaining coherence without creating bottlenecks.
  • Reviews architectural implementations against intent and closes the feedback loop - updating standards when reality reveals better approaches.

Quality & Craft

  • Produces architectural documentation that is clear, precise, and durable - written for engineers who need to implement it and leaders who need to fund it.
  • Designs architectures that prioritise long-term maintainability - resisting the pressure to optimise for speed of delivery at the cost of structural integrity.
  • Applies systems thinking rigorously - modelling interdependencies, failure modes, and evolution paths before committing to significant architectural directions.
  • Reviews data models for correctness and long-term fitness - not just whether they solve the immediate problem but whether they will scale and remain coherent as the business evolves.
  • Establishes quality gates for architectural compliance - ensuring teams have clear checkpoints to validate alignment with enterprise standards.
  • Holds high standards for data modelling - naming, normalisation, documentation - and enforces them consistently across the organisation.

Communication

  • Communicates enterprise architecture to executive audiences clearly - translating structural complexity into business risk, opportunity, and investment language.
  • Writes architecture standards and decision records that are precise enough to be implemented correctly and accessible enough to be understood without hand-holding.
  • Facilitates architecture review sessions that reach clear, well-reasoned decisions - not design-by-committee or decisions deferred indefinitely.
  • Builds influential relationships with engineering leaders, business stakeholders, and technology vendors through consistent credibility and clear thinking.
  • Communicates the "why" behind architectural constraints - ensuring engineers understand the reasoning, not just the rules.
  • Presents complex trade-offs fairly - including the costs of the recommended approach, not just the costs of alternatives.

Collaboration

  • Partners effectively with engineering leaders - VPs of Engineering, Heads of Data, Engineering Managers - to align architecture with organisational priorities.
  • Collaborates with Senior Platform Engineers and Platform Architects to ensure data and platform architectures are mutually coherent.
  • Works closely with data governance and compliance stakeholders - legal, risk, privacy - to incorporate their requirements without compromising engineering practicality.
  • Builds a data architecture community of practice - connecting senior data engineers across teams and creating shared ownership of architectural standards.
  • Engages with product leadership to understand strategic data needs early - shaping architecture investment before requirements are fixed.
  • Represents the organisation at industry forums and external events, building reputation and bringing external perspectives inward.

Ownership

  • Owns the coherence of the organisation's data architecture - accountable for its long-term structural health, not just individual decisions.
  • Takes responsibility for architectural debt - identifying it, quantifying it, and advocating for investment in resolving it.
  • Drives governance of the data estate with genuine authority - escalating to senior leadership when architectural standards are being systematically bypassed.
  • Ensures architectural decisions are durable - not just fit for today's business context but designed to evolve gracefully as the organisation changes.
  • Owns the organisation's data technology radar - keeping it current, evidence-based, and actionable.
  • Holds themselves accountable for the adoption of architectural standards - not just defining them but ensuring they are understood, implementable, and implemented.

Technical Foundation

  • Maintains deep, current expertise in enterprise data architecture - including the latest thinking on data mesh, data contracts, semantic modelling, and real-time data architectures.
  • Understands the engineering realities behind architectural patterns - able to reason about implementation complexity, operational overhead, and failure modes at a detailed level.
  • Demonstrates breadth across the full data technology landscape - from raw storage and compute through orchestration, transformation, cataloguing, quality, and serving.
  • Applies data governance frameworks with practical engineering sense - translating governance requirements into implementable engineering patterns.
  • Understands the economics of data infrastructure - cloud costs, tooling licensing, operational overhead - well enough to advise on financially sound architectural choices.
  • Keeps pace with the evolution of AI/ML as a consumer and shaper of data architecture - ensuring the data estate is fit for the organisation's AI ambitions.
  • Maintains sufficient hands-on capability to credibly review engineering implementations and earn the trust of the engineering teams they advise.
Skills
Deep expertise in enterprise data architecture patterns - data mesh, data lakehouse, hub-and-spoke, data fabric - with ability to apply them to real organisational contexts.
Advanced data modelling capability - dimensional, relational, entity-relationship, and ontological modelling at enterprise scale.
Strong understanding of data governance frameworks - DAMA-DMBOK, DCAM, or equivalent - and their practical application in engineering organisations.
Experience designing data lineage and cataloguing solutions at enterprise scale.
Broad knowledge of the data technology landscape - cloud platforms, orchestration tools, transformation frameworks, cataloguing tools, quality platforms.
Experience with regulatory and compliance requirements affecting data - GDPR, data residency, financial services data requirements.
Ability to communicate architectural concepts clearly to executive and non-technical audiences.
Strong analytical and systems thinking skills - ability to model complex interdependencies and reason about second-order effects.
AI AI & Automation Expectations Updated for the AI-augmented era

AI Augmented Delivery

  • Shapes the organisation's strategy for AI-augmented data work - defining where AI tools accelerate data engineering responsibly and where human expert judgement is non-negotiable.
  • Evaluates AI-powered data governance tools - AI-driven data cataloguing, automated lineage capture, intelligent data quality classification - as part of the technology selection process.
  • Uses AI to accelerate architectural documentation - generating initial drafts of architecture decision records, data model documentation, and governance framework descriptions - then applies expert refinement to ensure accuracy and completeness.
  • Assesses the architectural implications of AI/ML workloads - feature stores, training data management, model serving patterns - ensuring the data architecture supports the organisation's AI ambitions.
  • Defines standards for responsible use of AI in data engineering - including data provenance, explainability requirements, and governance of AI-generated data transformations.
  • Uses AI for scenario modelling and architecture stress testing - generating questions, edge cases, and failure scenarios to pressure-test architectural proposals before committing to them.