Engineering Director
Accountable for directing the whole engineering Function or several Heads and for its budget and strategy.
3tasks in this job
43skills it uses
5tools named
The skills this job leans on most
- 1Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 3 tasks
- 2Planning delivery dependencies and the critical pathNeeded in 1 of 3 tasks
- 3Verifying benefits realisation and attributionNeeded in 1 of 3 tasks
ToolsMicrosoft ExcelJiraMicrosoft Power BIServiceNowMicrosoft PowerPoint
All 3 tasks, where people get stuck and all 43 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Maintain a data and AI risk register and record risk treatment and acceptance
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Maintaining a risk register with owners, triggers and responses
- Rating risks against appetite and choosing proportionate treatment
All skills
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- 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
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Constructing clear and honest charts
- Designing and comparing decision scenarios
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining technical work to accountable non-specialists
- Identifying and quantifying decision downside and exposure
- Interpreting correlation without inferring cause
- 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
- Monitoring model fitness and drift
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Coming soonDelivering Data and AI Projects with EvidenceManaging Data and AI Products
6tasks in this job
72skills it uses
5tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 5 of 6 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 4 of 6 tasks
- 3Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 6 tasks
- 4Planning delivery dependencies and the critical pathNeeded in 1 of 6 tasks
- 5Verifying benefits realisation and attributionNeeded in 1 of 6 tasks
ToolsMicrosoft ExcelMicrosoft PowerPointMicrosoft Azure (portal, Cost Management, Monitor)Microsoft Power BIServiceNow
All 6 tasks, where people get stuck and all 72 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Define, monitor and review data-service reliability objectives
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Verifying benefits realisation and attribution
- Analysing error patterns and failure modes in models and AI systems
- 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
- Building cohort and retention analyses with correct denominators
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- 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 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- 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
- 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
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- 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
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 EvidenceManaging Data and AI Products
7tasks in this job
81skills it uses
5tools named
The skills this job leans on most
- 1Rating risks against appetite and choosing proportionate treatmentNeeded in 6 of 7 tasks
- 2Prioritising work by evidenced value, cost, risk and confidenceNeeded in 4 of 7 tasks
- 3Maintaining a risk register with owners, triggers and responsesNeeded in 1 of 7 tasks
- 4Planning delivery dependencies and the critical pathNeeded in 1 of 7 tasks
- 5Verifying benefits realisation and attributionNeeded in 1 of 7 tasks
ToolsMicrosoft ExcelMicrosoft PowerPointMicrosoft Power BIOneTrustServiceNow
All 7 tasks, where people get stuck and all 81 skills
Tasks
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Define, monitor and review data-service reliability objectives
- 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
- Present data, AI, risk and performance evidence to the executive committee or board and record the decisions taken
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
All skills
- Maintaining a risk register with owners, triggers and responses
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Verifying benefits realisation and attribution
- Analysing error patterns and failure modes in models and AI systems
- 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 data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing clear and honest charts
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- 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 enforceable data governance policies
- Diagnosing misleading data visualisations
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Explaining technical work to accountable non-specialists
- Facilitating group decisions using disputed evidence
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Independently checking analytical conclusions and claims
- 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 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- 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
- 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
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Working through stakeholder conflict over evidence and responsibility
- 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
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 EvidenceManaging Data and AI Products
About the role
Accountable for directing the whole engineering Function or several Heads and for its budget and strategy. Sets technology strategy for the Function and decides which platforms and AI capabilities to fund. Differs from a Head of Engineering, who runs the operating model for one engineering discipline.
Also called VP Engineering