Data Architect
Accountable for the structure of bounded data and analytics services, including data models, integration patterns and the trade-offs between them.
2tasks in this job
7skills it uses
6tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 2 of 2 tasks
- 2Explaining data and AI platform architecture patterns and building blocksNeeded in 2 of 2 tasks
- 3Designing and versioning data contracts for consumersCore to this role
ToolsMicrosoft Azure (portal, Cost Management, Monitor)Amazon Web Services (console, Cost Explorer, CloudWatch)JiraMicrosoft ExcelMicrosoft PurviewCollibraerwin Data ModelerER/StudioSnowflakeDatabricksSQL (query language)SAP S/4HANA
All 2 tasks, where people get stuck and all 7 skills
Tasks
Where people get stuck
All skills
- Explaining data and AI platform architecture patterns and building blocks
- Applying data classification and handling rules
- Comparing options against explicit decision criteria
- Designing semantic models for governed analytics
- Recognising personal data and privacy duties
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonData Platform Architecture, Integration and Economics
6tasks in this job
93skills it uses
12tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 2 of 6 tasks
- 2Designing and versioning data contracts for consumersNeeded in 2 of 6 tasks
- 3Explaining data and AI platform architecture patterns and building blocksNeeded in 2 of 6 tasks
ToolsMicrosoft Azure (portal, Cost Management, Monitor)Amazon Web Services (console, Cost Explorer, CloudWatch)JiraMicrosoft ExcelMicrosoft PurviewCollibra+ 6 more
All 6 tasks, where people get stuck and all 93 skills
Tasks
- Evaluate platform performance and cost
- Prepare and verify a data-service release or migration for change approval
- Design and build a tested, governed analytical data model
- Architect a bounded data and analytics service
- Evaluate and recommend a data, analytics or AI product or vendor
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
All skills
- Designing and versioning data contracts for consumers
- Evaluating software architecture for data and AI systems
- 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
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing dimensional models with explicit grain
- Designing efficient and reproducible investigations
- Designing relational schemas: keys, constraints and normalisation
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing operational handoffs and flow
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Evaluating supplier total cost and value
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting policy scope and delegated authority limits
- 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 external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- 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 personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- 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
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonData Platform Architecture, Integration and Economics
6tasks in this job
103skills it uses
11tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 3 of 6 tasks
- 2Explaining data and AI platform architecture patterns and building blocksNeeded in 3 of 6 tasks
- 3Designing and versioning data contracts for consumersNeeded in 2 of 6 tasks
- 4Designing decision rights and dispute routes for data and AI workCore to this role
ToolsMicrosoft Azure (portal, Cost Management, Monitor)Amazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Power BIMicrosoft ExcelSnowflakeDatabricks+ 5 more
All 6 tasks, where people get stuck and all 103 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Design and build a tested, governed analytical data model
- Architect a bounded data and analytics service
- 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
- Designing and versioning data contracts for consumers
- Evaluating software architecture for data and AI systems
- 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
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing dimensional models with explicit grain
- Designing efficient and reproducible investigations
- Designing relational schemas: keys, constraints and normalisation
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- 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
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Evaluating supplier total cost and value
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting policy scope and delegated authority limits
- 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 external evidence applies to the decision context
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Azure (portal, Cost Management, Monitor)
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Power BI
- Microsoft Excel
- Snowflake
- Databricks
- erwin Data Modeler
- ER/Studio
- Microsoft SharePoint
- Microsoft Purview
- Collibra
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
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonData Platform Architecture, Integration and Economics
About the role
Accountable for the structure of bounded data and analytics services, including data models, integration patterns and the trade-offs between them. Makes design decisions that must meet business requirements, governance rules and cost limits. Engineers implement the designs, while the Architect sets the structure and standards they follow.
Also called Data Modeller