Role

Data Architect

Level 1
Unsatisfactory
Low
Individual
Impact
  • Fails to define or maintain a coherent enterprise data architecture; teams operate without clear structural guidance.
  • Data governance framework is absent or non-functional; ownership, lineage, and quality standards are undefined.
  • Technology strategy decisions are made without evidence or trade-off analysis, creating costly and inconsistent platform choices.
  • Architectural artefacts - ADRs, target state diagrams, standards documents - are absent or out of date.
Examples
  • Multiple data engineering teams implemented conflicting data product patterns with no architectural guidance for a full quarter.
  • A significant data platform tooling change was approved without a documented trade-off analysis or total cost of ownership assessment.
Dampeners
  • Was appointed to a newly created role with no prior data architecture function; the starting position was effectively a blank canvas.
  • Significant organisational change during this period removed the stakeholder context needed to drive architectural direction.
Progression Signal
  • Publishes one clear enterprise data architecture standard or ADR that engineering teams can implement consistently.
  • Initiates a data governance forum with defined scope, accountability, and cadence.
Business Impact
Impact
  • Engineering teams make inconsistent architectural decisions that accumulate as technical debt and integration friction.
  • Data governance gaps create regulatory and compliance risk that may not be immediately visible but is compounding.
Examples
  • A compliance audit identified 12 data assets without defined ownership or lineage, requiring an emergency remediation programme.
Dampeners
  • Some governance gaps may pre-date this individual's appointment; attribution requires careful analysis.
Progression Signal
  • Engineering teams begin making more consistent architectural decisions; compliance risk posture improves.
Mid
Individual
Impact
  • Produces architectural artefacts but they lack the rigour needed for consistent implementation - ambiguous standards, underdeveloped governance frameworks.
  • Technology recommendations are made without sufficient evidence; the data technology radar is absent or stale.
  • Cross-organisational influence is limited; architecture standards are not being adopted by all data engineering teams.
Examples
  • Published a data mesh standard that was inconsistently interpreted across four teams due to insufficient specificity.
  • Data technology radar had not been updated for six months despite significant changes in the tooling landscape.
Dampeners
  • Developing into the full scope of a complex enterprise architecture role; rigour is expected to improve with tenure.
Progression Signal
  • Publishes a revised standard with sufficient specificity for consistent implementation across teams.
  • Updates the technology radar with current, evidence-based guidance.
Business Impact
Impact
  • Ambiguous architecture standards create inconsistent implementation and platform fragmentation across data teams.
  • Stale technology guidance leads engineering teams to make suboptimal tooling decisions.
Examples
  • Three teams independently adopted different orchestration approaches due to the absence of a current technology standard.
Dampeners
  • Business impact is architectural and accumulating; immediate delivery impact may not yet be severe.
Progression Signal
  • Platform fragmentation reduces; engineering teams cite the architecture guidance as useful and actionable.
High
Individual
Impact
  • Architecture standards are being produced but are not being adopted; influence over engineering teams is ineffective.
  • Data governance accountability structures are defined but not enforced; data quality and ownership remain unclear.
  • Resistance to feedback from engineering leadership about architectural direction is creating friction and distrust.
Examples
  • Published three data architecture standards in a quarter; none were adopted by the engineering teams they targeted.
  • Data governance forum had met monthly for six months but had made no binding decisions or enforced any standards.
Dampeners
  • Formal support structure and structured development plan should be in place before escalating further.
Progression Signal
  • At least one architectural standard is adopted and implemented consistently by a data engineering team.
Business Impact
Impact
  • Absence of effective architectural governance creates a fragmented data estate that is costly to maintain and evolve.
  • Engineering confidence in the architecture function is eroding, reducing its ability to provide strategic guidance.
Examples
  • Head of Engineering noted they had stopped routing architectural decisions through the data architecture review due to lack of useful guidance.
Dampeners
  • Business impact is structural and long-term; requires sustained attention rather than a single intervention.
Progression Signal
  • Engineering leadership re-engages with architecture review; architectural guidance begins to be cited in team decisions.
Level 2
Development Needed
Low
Individual
Impact
  • Produces enterprise data architecture guidance but with gaps in completeness - governance, compliance, and lineage requirements are underdeveloped.
  • Technology strategy is present but not regularly reviewed; tooling recommendations lag behind the evolving platform landscape.
  • Engagement with senior engineering and business leadership is limited; architectural direction is not well-socialised.
Examples
  • Published a target state data architecture without addressing data lineage, access control, or regulatory compliance requirements.
  • Technology radar had not been reviewed in a quarter despite two major tooling decisions being made in that period.
Dampeners
  • Developing the full breadth of enterprise architecture scope takes time; this is expected to improve with experience.
Progression Signal
  • Begins incorporating governance, lineage, and compliance requirements into all architectural proposals as a default.
  • Establishes a regular technology radar review cadence tied to engineering planning cycles.
Business Impact
Impact
  • Architecture gaps in governance and compliance create regulatory risk that may not surface until an audit or incident.
  • Lagging technology guidance leads teams to make platform decisions without adequate architectural steer.
Examples
  • A data platform investment was made without architectural review; it was later found to conflict with the target state architecture.
Dampeners
  • At this tenure level, the expectation is that these gaps are actively closing.
Progression Signal
  • Regulatory risk posture improves; engineering teams begin routing major technology decisions through the architecture function.
Mid
Individual
Impact
  • Enterprise architecture guidance is present but does not adequately address the organisation's AI and analytics ambitions.
  • Architecture review process exists but is inconsistently applied; some significant platform decisions bypass architectural governance.
  • Communication of architectural direction to executive and non-technical audiences is not yet effective.
Examples
  • AI/ML platform architecture requirements were not incorporated into the data architecture despite a major machine learning programme commencing.
  • Two significant data platform decisions in a quarter were made without architectural review due to unclear governance process.
Dampeners
  • AI architecture integration is a developing area; external expertise and peer network engagement may accelerate this.
Progression Signal
  • Begins proactively engaging with AI/ML teams to incorporate their requirements into the data architecture.
  • Architecture governance process becomes consistent; no significant platform decisions bypass review.
Business Impact
Impact
  • Data architecture misaligned with AI/ML requirements creates rework costs as the machine learning programme scales.
  • Architecture governance gaps allow platform decisions that conflict with the target state, increasing remediation costs.
Examples
  • A feature store implementation had to be redesigned six months in because data architecture requirements were not defined upfront.
Dampeners
  • Some of these gaps reflect organisational coordination challenges rather than individual architectural failure.
Progression Signal
  • AI/ML platform requirements are incorporated into the architecture; governance compliance improves.
High
Individual
Impact
  • Architecture guidance is produced but not adopted at the pace required; influence over the engineering organisation is developing slowly.
  • Data governance accountability is defined but weak; data quality and ownership standards are not being enforced consistently.
  • Architectural decisions in adjacent domains - application architecture, platform engineering - are not being engaged with or connected.
Examples
  • Data governance standards published eighteen months ago are still not adopted by two major data engineering teams.
  • A platform architecture decision with significant data implications was made without engagement from the data architecture function.
Dampeners
  • Influence and adoption at enterprise scale takes time; the pace of progress matters more than the current state alone.
Progression Signal
  • One previously non-compliant team adopts a data architecture standard with a clear implementation plan.
Business Impact
Impact
  • Slow adoption of governance standards means regulatory and quality risk continues to accumulate.
  • Disconnection from adjacent architecture domains creates integration and compatibility risks in the wider technical estate.
Examples
  • An application platform migration introduced data architecture incompatibilities because data architectural requirements were not engaged early.
Dampeners
  • Enterprise adoption challenges are structural; systemic intervention may be needed alongside individual development.
Progression Signal
  • Governance adoption rate improves; adjacent architecture integration issues begin to reduce.
Level 3
Consistently Delivers
Low
Individual
Impact
  • Defines and maintains a coherent enterprise data architecture - structural patterns, modelling standards, integration principles - that engineering teams implement consistently.
  • Operates a functional data governance framework - ownership, lineage, quality standards, cataloguing - with defined accountability across the organisation.
  • Maintains a current, evidence-based data technology radar that provides actionable guidance to engineering teams.
Examples
  • Published and socialised an enterprise data modelling standard that all four data engineering teams now follow.
  • Established a data governance forum with defined ownership assignments for all critical data domains within one quarter.
Dampeners
  • Architecture adoption is consistent within the data engineering function; broader cross-organisational influence is still developing.
Progression Signal
  • Architecture guidance begins to be adopted voluntarily by teams outside the data engineering function.
Business Impact
Impact
  • Consistent architectural standards reduce engineering variability, lower integration costs, and improve data platform reliability.
  • Data governance framework reduces regulatory risk and enables faster compliance responses.
Examples
  • Data governance framework they established enabled a GDPR subject access request to be fulfilled in one day rather than two weeks.
Dampeners
  • Business impact is strong within the data function; cross-organisational executive visibility is developing.
Progression Signal
  • Executive stakeholders begin citing the architecture function as a strategic enabler.
Mid
Individual
Impact
  • Leads the organisation's data architecture strategy - defining the target state, sequencing investment, and aligning engineering delivery to the long-term vision.
  • Connects data architecture to AI/ML and analytics ambitions, ensuring the data estate supports the organisation's strategic priorities.
  • Builds architectural capability across the data engineering organisation - coaching senior engineers, developing architecture review processes.
Examples
  • Developed a three-year data architecture roadmap aligned to the organisation's analytics strategy, adopted by the data leadership team.
  • Designed a feature store architecture that enabled the machine learning team to iterate at twice the previous speed.
Dampeners
  • Continuing to develop executive communication and cross-organisation influence to the most senior levels.
Progression Signal
  • Executive leadership begins citing the data architecture strategy as a key element of the organisation's data capability.
Business Impact
Impact
  • Data architecture roadmap provides a credible investment framework that enables multi-year platform planning.
  • AI/ML platform integration delivers measurable acceleration to the organisation's analytics and intelligence programmes.
Examples
  • The machine learning programme delivered two products in six months that previously took a year, enabled by the feature store architecture.
Dampeners
  • Business impact is strong and executive-visible; growing toward board-level strategic recognition.
Progression Signal
  • Data architecture investments they define are cited in executive and board-level strategy conversations.
High
Individual
Impact
  • Is the definitive technical authority for data architecture across the organisation - their decisions shape the data estate with confidence and credibility.
  • Drives data governance and compliance architecture that meets the organisation's regulatory requirements with engineering practicality.
  • Is building the next generation of data architects - coaching senior data engineers toward architectural leadership.
Examples
  • Defined and implemented a data lineage architecture that satisfied GDPR, financial services, and internal audit requirements simultaneously.
  • Developed two senior data engineers toward architectural leadership roles through structured coaching and increasing accountability.
Dampeners
  • Still developing the full scope of external representation and industry thought leadership characteristic of the role ceiling.
Progression Signal
  • Is beginning to represent the organisation at industry forums and contribute to external data architecture discourse.
Business Impact
Impact
  • Data governance and compliance architecture they design materially reduces the organisation's regulatory risk exposure.
  • Senior engineer development they drive creates a sustainable pipeline of architectural capability for the organisation.
Examples
  • Regulatory compliance architecture they designed received explicit positive recognition in an external audit report.
Dampeners
  • Business impact is strong; growing toward full strategic visibility at the most senior organisational levels.
Progression Signal
  • Executive and board-level stakeholders cite data architecture as a strategic organisational strength.
Level 4
Leading
Low
Individual
Impact
  • Defines the enterprise data architecture vision and drives its adoption across the full engineering organisation - not just the data function.
  • Data governance framework is authoritative and operational; data ownership, quality standards, and lineage are enforced with genuine accountability.
  • Technology strategy positions the organisation confidently for multi-year platform evolution, with a current and well-reasoned data technology radar.
Examples
  • Enterprise data architecture vision was formally endorsed by the CTO and adopted as a strategic framework across all technology teams.
  • Data technology radar recommendations directly shaped two major platform investment decisions, saving significant future remediation costs.
Dampeners
  • Full board-level data strategy influence and external industry thought leadership are developing.
Progression Signal
  • Is contributing to board-level data strategy conversations as a recognised technical authority.
Business Impact
Impact
  • Enterprise architecture vision creates a compounding platform investment framework that aligns technology choices with multi-year strategic outcomes.
  • Authoritative governance framework reduces regulatory risk and enables faster, more confident use of data across the organisation.
Examples
  • Enterprise data architecture framework they defined enabled a major acquisition integration in half the previously estimated time.
Dampeners
  • Business impact is strong and executive-visible; growing toward board-level strategic recognition.
Progression Signal
  • Board-level stakeholders cite data architecture as a strategic competitive advantage.
Mid
Individual
Impact
  • Is the organisation's foremost data architecture authority - shaping strategy, governing standards, and building a community of practice across data teams.
  • Connects data architecture to the organisation's AI, analytics, and digital ambitions, ensuring the data estate is built to enable the business's future.
  • Builds data architectural capability at organisational scale - architecture review processes, communities of practice, coaching programmes.
Examples
  • Established an organisation-wide data architecture community of practice, connecting practitioners across 12 teams.
  • Designed a data platform strategy that positioned the organisation to support both operational analytics and machine learning at enterprise scale.
Dampeners
  • Board-level strategic influence and external industry leadership are developing; not yet fully established.
Progression Signal
  • Board-level data strategy is shaped by this individual's architectural thinking.
Business Impact
Impact
  • Organisation-wide data architecture alignment creates compounding value - lower integration costs, faster capability development, stronger regulatory posture.
  • AI and analytics platform investments they define deliver measurable acceleration to the organisation's intelligence programmes.
Examples
  • Data platform strategy they authored was used as the basis for a 5-year technology investment case approved at board level.
Dampeners
  • Business impact is at organisation scale and board-visible; this level of impact is at the role ceiling.
Progression Signal
  • Is recognised externally as well as internally as a leader in enterprise data architecture.
High
Individual
Impact
  • Operating at or beyond the role ceiling - their architectural leadership and strategic influence are defining the organisation's long-term data capability.
  • Is a recognised external voice in enterprise data architecture - contributing to industry discourse, conferences, and peer networks.
  • Defines not just the organisation's data architecture but shapes its approach to data as a strategic asset.
Examples
  • Published an enterprise data strategy framework adopted as a reference model by peer organisations in the sector.
  • Represented the organisation at a major industry conference, presenting an architecture approach that generated significant external engagement.
Dampeners
  • This level of impact within a single role may indicate that a broader leadership or advisory role is the right next step.
Progression Signal
  • Broader organisational or industry leadership opportunity is being explored - this level of impact should not be constrained by a single role.
Business Impact
Impact
  • Delivering data architecture and governance value at a level that creates measurable competitive advantage for the organisation.
  • External recognition builds the organisation's reputation as a data engineering and architecture centre of excellence.
Examples
  • Named by industry analysts as an organisation leading in enterprise data architecture practices, partly attributed to this individual's published work.
Dampeners
  • This is the ceiling of what a Data Architect role can deliver; broader scope is the right next step.
Progression Signal
  • Exploration of principal architect, advisory, or broader leadership opportunities is appropriate and warranted.
Level 5
Transformative
Low
Individual
Impact
  • Performing well beyond the Data Architect role - their architectural vision is shaping the organisation's data strategy at the most senior levels.
  • Is a recognised thought leader in enterprise data architecture - influencing the discipline externally as well as internally.
  • Has built a data architecture function from the ground up, or transformed a failing one into a high-functioning strategic capability.
Examples
  • Data architecture framework they defined was adopted as a sector standard, referenced by four peer organisations.
  • Built the organisation's data architecture function from zero to a team of three architects with a full governance structure in 18 months.
Dampeners
  • Impact at this level reflects exceptional circumstances as much as exceptional performance; context matters.
Progression Signal
  • A broader leadership or advisory role is the appropriate next step; this level of impact should not persist without formal recognition.
Business Impact
Impact
  • Data architecture and governance contributions create multi-year strategic value at organisational and industry scale.
  • The organisation's data capability is recognised as a competitive differentiator, partly as a direct result of this individual's work.
Examples
  • Data strategy and architecture work they led was cited in the organisation's annual report as a key strategic capability.
Dampeners
  • Exceptional impact that reflects the combination of individual performance and organisational context.
Progression Signal
  • Broader leadership recognition - fellowship, principal architect, advisory role - is the appropriate next step.
Mid
Individual
Impact
  • Anomalously strong even by senior Data Architect standards - defining the discipline as well as practising it.
  • Their architectural frameworks and governance approaches are treated as reference models inside and outside the organisation.
  • Shapes the careers of multiple architects and senior data engineers, with visible compounding impact on the discipline.
Examples
  • Wrote and published an enterprise data governance handbook that became the standard reference for practitioners in the sector.
  • Developed three senior data engineers to architect level over three years through structured mentoring and progressive responsibility.
Dampeners
  • This level of performance is very rare; it should trigger a formal recognition and career discussion.
Progression Signal
  • Fellowship, principal architect, or equivalent senior technical leadership recognition is appropriate and overdue.
Business Impact
Impact
  • Organisation-level and industry-level data architecture contributions create compounding strategic value.
  • External recognition builds the organisation's reputation as a place where the data engineering discipline is defined and advanced.
Examples
  • External architectural frameworks they authored were cited by a regulator as a model approach for data governance in the sector.
Dampeners
  • Impact at this level is exceptional; it should not persist without equivalent formal recognition.
Progression Signal
  • Industry leadership recognition and formal seniority advancement are the right responses to this level of performance.
High
Individual
Impact
  • Performing at a level that transcends the Data Architect role - their contributions define the enterprise data architecture discipline.
  • Their governance frameworks, architectural patterns, and published thinking are used as reference standards across the industry.
  • Represents an extreme outlier - an architect whose impact extends far beyond any single organisation.
Examples
  • Authored an open-source data governance framework adopted by over 50 organisations globally.
  • Invited to serve on an industry standards body shaping enterprise data architecture practices.
Dampeners
  • This level of impact is extraordinary; the organisation's ability to retain and reward appropriately is the primary risk.
Progression Signal
  • Fellowship, principal architect, advisory board, or equivalent recognition is the only appropriate response.
Business Impact
Impact
  • The organisation benefits from being associated with this individual's work - attracting talent, building reputation, and influencing strategy.
  • Their contributions create value that extends well beyond the organisation to the broader data engineering ecosystem.
Examples
  • The organisation's data architecture programme, led by this individual, was cited by analysts as an industry-leading case study.
Dampeners
  • Retaining this individual requires commensurate recognition; failure to act is a significant retention risk.
Progression Signal
  • Formal recognition at the highest available level is the only appropriate response to this level of contribution.