Head of Data Architecture
Accountable for the data architecture principles, roadmap and investment case across data and analytics.
3tasks in this job
41skills it uses
7tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 3 of 3 tasks
- 2Explaining data and AI platform architecture patterns and building blocksNeeded in 3 of 3 tasks
- 3Designing decision rights and dispute routes for data and AI workCore to this role
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft Power BIerwin Data ModelerServiceNowJiraMicrosoft Excel
All 3 tasks, where people get stuck and all 41 skills
Tasks
- Evaluate platform performance and cost
- Architect a bounded data and analytics service
- Define the target data architecture, principles and roadmap
Where people get stuck
All skills
- Explaining data and AI platform architecture patterns and building blocks
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Evaluating service performance and quality trade-offs
- Exploring data to find patterns that survive validation
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and resolving ethical conflicts in data and AI work
- Recognising personal data and privacy duties
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonData Platform Architecture, Integration and Economics
8tasks in this job
82skills it uses
9tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 4 of 8 tasks
- 2Explaining data and AI platform architecture patterns and building blocksNeeded in 3 of 8 tasks
- 3Designing and versioning data contracts for consumersNeeded in 2 of 8 tasks
- 4Designing decision rights and dispute routes for data and AI workCore to this role
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft Power BIMicrosoft ExcelServiceNowerwin Data Modeler+ 3 more
All 8 tasks, where people get stuck and all 82 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Assess continuity and third-party dependency risk for a data or AI service
- Design and build a tested, governed analytical data model
- Architect a bounded data and analytics service
- Maintain a data and AI risk register and record risk treatment and acceptance
- Evaluate and recommend a data, analytics or AI product or vendor
- Define the target data architecture, principles and roadmap
Where people get stuck
All skills
- Explaining data and AI platform architecture patterns and building blocks
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Exploring data to find patterns that survive validation
- Gating releases on evidence with rollback ready
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying benefits realisation and attribution
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Azure (portal, Cost Management, Monitor)
- Microsoft Power BI
- Microsoft Excel
- ServiceNow
- erwin Data Modeler
- Collibra
- Microsoft Purview
- Jira
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonData Platform Architecture, Integration and Economics
7tasks in this job
101skills it uses
9tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 4 of 7 tasks
- 2Explaining data and AI platform architecture patterns and building blocksNeeded in 3 of 7 tasks
- 3Designing decision rights and dispute routes for data and AI workNeeded in 1 of 7 tasks
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft ExcelMicrosoft PowerPointMicrosoft Power BIServiceNow+ 3 more
All 7 tasks, where people get stuck and all 101 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Assess continuity and third-party dependency risk for a data or AI service
- Architect a bounded data and analytics service
- Design and govern the operating model for data, analytics and AI work in a function
- Maintain a data and AI risk register and record risk treatment and acceptance
- Define the target data architecture, principles and roadmap
Where people get stuck
All skills
- Explaining data and AI platform architecture patterns and building blocks
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating fitness and full cost of AI-assisted work
- Evaluating product outcomes and metric integrity
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Exploring data to find patterns that survive validation
- Facilitating group decisions using disputed evidence
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting customer feedback and qualitative evidence
- Interpreting supplier contract duties, terms and exposure
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Revising prior decisions when evidence changes
- Selecting charts for evidence and decisions
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Azure (portal, Cost Management, Monitor)
- Microsoft Excel
- Microsoft PowerPoint
- Microsoft Power BI
- ServiceNow
- erwin Data Modeler
- Collibra
- Microsoft Purview
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonData Platform Architecture, Integration and Economics
About the role
Accountable for the data architecture principles, roadmap and investment case across data and analytics. Decides architecture investment within delegated budget and which architecture risks the organisation accepts. Leads data architects directly and holds them to agreed standards, where a Data Architect sets the structure of individual services.
Also called Head of Enterprise Data Architecture