Head of Engineering
Accountable for engineering across several teams and for its architecture direction, reliability posture and investment.
5tasks in this job
100skills it uses
6tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 5 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 1 of 5 tasks
- 3Verifying benefits realisation and attributionNeeded in 1 of 5 tasks
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft ExcelDatabricksMicrosoft Power BIServiceNowMicrosoft PowerPointJira
All 5 tasks, where people get stuck and all 100 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Lead an incident through containment, restoration and communication
- Maintain a data and AI risk register and record risk treatment and acceptance
- Measure engineering delivery performance and set improvement priorities
Where people get stuck
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Monitoring data quality and freshness in production
- Assigning and operating data ownership and stewardship
All skills
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Verifying benefits realisation and attribution
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Balancing territories and quotas from account potential
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- 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
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining data and AI platform architecture patterns and building blocks
- Explaining technical work to accountable non-specialists
- 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 correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- 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 proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Selecting charts for evidence and decisions
- 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
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying project progress against accepted outcomes
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
11tasks in this job
121skills it uses
8tools named
The skills this job leans on most
- 1Monitoring data quality and freshness in productionNeeded in 4 of 11 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 4 of 11 tasks
- 3Setting and reviewing reliability objectives and error budgets for data and AI servicesNeeded in 3 of 11 tasks
- 4Assigning and operating data ownership and stewardshipNeeded in 2 of 11 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 11 tasks
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft ExcelDatabricksMicrosoft Power BIServiceNow+ 2 more
All 11 tasks, where people get stuck and all 121 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Lead an incident through containment, restoration and communication
- Define, monitor and review data-service reliability objectives
- Assess continuity and third-party dependency risk for a data or AI service
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Evaluate and recommend a data, analytics or AI product or vendor
- Set engineering standards for testing, change, data handling and AI-assisted code
- Measure engineering delivery performance and set improvement priorities
Where people get stuck
- Reviewing human- and AI-generated code changes for correctness and risk
- Designing testable data controls
- Writing testable work standards and review checklists
All skills
- Assigning and operating data ownership and stewardship
- Defining measurable outcomes and leading indicators
- Monitoring data quality and freshness in production
- Prioritising work by evidenced value, cost, risk and confidence
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- 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 and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- 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 and improving chart accessibility
- 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
- Explaining data and AI platform architecture patterns and building blocks
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- 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
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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 and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preparing change records with impact assessment and rollback
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Variance analysis
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- 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 soonDelivering Data and AI Projects with EvidenceData Product Reliability: Release Gates, Service Levels and Incidents
11tasks in this job
124skills it uses
8tools named
The skills this job leans on most
- 1Reviewing human- and AI-generated code changes for correctness and riskNeeded in 10 of 11 tasks
- 2Setting and reviewing reliability objectives and error budgets for data and AI servicesNeeded in 2 of 11 tasks
- 3Monitoring data quality and freshness in productionNeeded in 2 of 11 tasks
- 4Assigning and operating data ownership and stewardshipNeeded in 2 of 11 tasks
- 5Designing testable data controlsCore to this role
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Microsoft ExcelDatabricksMicrosoft Power BIMicrosoft PowerPoint+ 2 more
All 11 tasks, where people get stuck and all 124 skills
Tasks
- Evaluate platform performance and cost
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Define, monitor and review data-service reliability objectives
- Prepare a periodic performance or risk report for a management or governance committee
- 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
- Evaluate and recommend a data, analytics or AI product or vendor
- Define the target data architecture, principles and roadmap
- Set engineering standards for testing, change, data handling and AI-assisted code
- Measure engineering delivery performance and set improvement priorities
Where people get stuck
- Reviewing human- and AI-generated code changes for correctness and risk
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Assigning and operating data ownership and stewardship
- Monitoring data quality and freshness in production
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing queue behaviour, capacity and service constraints
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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
- Balancing territories and quotas from account potential
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- 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 rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- 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 and improving chart accessibility
- 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
- Explaining data and AI platform architecture patterns and building blocks
- Explaining technical work to accountable non-specialists
- 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
- Influencing change without formal authority
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- 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 and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Judging whether uncertainty changes the decision
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preparing change records with impact assessment and rollback
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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
- 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
- Working through stakeholder conflict over evidence and responsibility
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Azure (portal, Cost Management, Monitor)
- Microsoft Excel
- Databricks
- Microsoft Power BI
- Microsoft PowerPoint
- ServiceNow
- 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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonDelivering Data and AI Projects with EvidenceData Product Reliability: Release Gates, Service Levels and Incidents
About the role
Accountable for engineering across several teams and for its architecture direction, reliability posture and investment. Decides where engineering capacity and budget go and which technology risks the function accepts. Data and AI judgement sits in deciding which platforms and AI capabilities to fund and how their risk is governed.
Also called Head of Software Development