Data Architect – Growth Tracker

[ Name ] Data Architect – Growth Tracker

DA  ·  SFIA 6-7  ·  raganmcgill.co.uk

1Novice
No evidence of this yet · Lacks experience in this competency · Requires significant training and guidance
2Developing
Evidence of trying but lacking consistency · Demonstrates effort and initial attempts · Progressing, consistency is needed
3Proficient
Evidence of doing this with areas for improvement · Competent with some areas for enhancement · Meets most expectations
4Accomplished
Evidence of consistently meeting expectations · Highly reliable in delivering results · Maintains performance standards
5Expert
Evidence of exceeding expectations · Demonstrates exceptional mastery · Autonomous · Leads and mentors others
Learning & Growth
Delivery
Quality & Craft
Communication
Collaboration
Ownership
Technical Foundation
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.
Evidence & examples
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Strengths to recognise

Development focus areas

Overall assessment & agreed actions

raganmcgill.co.uk  ·  Data Architect Growth Tracker