Engineering Manager
Accountable for an engineering team's delivery quality, its change and incident practice and the development of its engineers.
4tasks in this job
65skills it uses
6tools named
The skills this job leans on most
- 1Gating releases on evidence with rollback readyNeeded in 3 of 4 tasks
- 2Planning and validating controlled data migrationsNeeded in 3 of 4 tasks
- 3Preparing change records with impact assessment and rollbackNeeded in 2 of 4 tasks
- 4Assessing change risk, collisions and go or no-goNeeded in 2 of 4 tasks
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)JiraAzure DevOpsServiceNowDatadogMicrosoft 365 CopilotMicrosoft Power BIGit / GitHub
All 4 tasks, where people get stuck and all 65 skills
Tasks
- Prepare and verify a data-service release or migration for change approval
- Lead an incident through containment, restoration and communication
- Deliver a tested software or API change to a data or AI service
- Measure engineering delivery performance and set improvement priorities
Where people get stuck
- Escalating unresolved risks with evidence and clear ownership
- Running incident command: severity, timeline and status updates
- Preserving incident evidence and timeline
All skills
- Assessing change risk, collisions and go or no-go
- Gating releases on evidence with rollback ready
- Preparing change records with impact assessment and rollback
- Analysing error patterns and failure modes in models and AI systems
- Analysing queue behaviour, capacity and service constraints
- Applying data classification and handling rules
- Assessing data-service continuity and recoverability
- Balancing territories and quotas from account potential
- 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
- Constructing clear and honest charts
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing decision-oriented research with customers, users or employees
- Designing experiments
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating product outcomes and metric integrity
- Evaluating service performance and quality trade-offs
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting operational demand at interval and site grain
- Identifying keys and testing referential integrity
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Judging whether uncertainty changes the decision
- 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
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- 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 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
- Selecting charts for evidence and decisions
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonData Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics
8tasks in this job
119skills it uses
9tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 8 of 8 tasks
- 2Gating releases on evidence with rollback readyNeeded in 6 of 8 tasks
- 3Planning and validating controlled data migrationsNeeded in 5 of 8 tasks
- 4Preparing change records with impact assessment and rollbackNeeded in 3 of 8 tasks
- 5Running incident command: severity, timeline and status updatesNeeded in 2 of 8 tasks
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)JiraAzure DevOpsServiceNowDatadog+ 3 more
All 8 tasks, where people get stuck and all 119 skills
Tasks
- Evaluate platform performance and cost
- Control project or programme risks, issues, dependencies and change requests
- Prepare and verify a data-service release or migration for change approval
- Lead an incident through containment, restoration and communication
- Define, monitor and review data-service reliability objectives
- Deliver a tested software or API change to a data or AI service
- 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
- Escalating unresolved risks with evidence and clear ownership
- Gating releases on evidence with rollback ready
- Preparing change records with impact assessment and rollback
- Running incident command: severity, timeline and status updates
- 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
- 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 supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- 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 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- 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
- 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 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 data quality exceptions
- 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
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating product outcomes and metric integrity
- Evaluating service performance and quality trade-offs
- Evaluating software architecture for data and AI systems
- 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 evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Forecasting project cost and schedule from progress evidence
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- 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 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 data quality and freshness in production
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- 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 and tracing unfamiliar code
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Revising prior decisions when evidence changes
- 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
- 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
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- 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
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 Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics
9tasks in this job
130skills it uses
9tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 9 of 9 tasks
- 2Reviewing human- and AI-generated code changes for correctness and riskNeeded in 7 of 9 tasks
- 3Gating releases on evidence with rollback readyNeeded in 6 of 9 tasks
- 4Planning and validating controlled data migrationsNeeded in 5 of 9 tasks
- 5Designing testable data controlsCore to this role
ToolsAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)JiraServiceNowDatadogMicrosoft Power BI+ 3 more
All 9 tasks, where people get stuck and all 130 skills
Tasks
- Evaluate platform performance and cost
- Control project or programme risks, issues, dependencies and change requests
- Prepare and verify a data-service release or migration for change approval
- Lead an incident through containment, restoration and communication
- Define, monitor and review data-service reliability objectives
- Deliver a tested software or API change to a data or AI service
- Set engineering standards for testing, change, data handling and AI-assisted code
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Measure engineering delivery performance and set improvement priorities
Where people get stuck
- Reviewing human- and AI-generated code changes for correctness and risk
- Gating releases on evidence with rollback ready
- Escalating unresolved risks with evidence and clear ownership
All skills
- Escalating unresolved risks with evidence and clear ownership
- Gating releases on evidence with rollback ready
- Reviewing human- and AI-generated code changes for correctness and risk
- 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
- 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
- Assigning and operating data ownership and stewardship
- 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 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
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing options against explicit decision criteria
- 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
- 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 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 data quality exceptions
- 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
- Establishing and declaring data grain
- 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 software architecture for data and AI systems
- 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 evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting operational demand at interval and site grain
- Forecasting project cost and schedule from progress evidence
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and prioritising critical data elements
- 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 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 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 data quality and freshness in production
- Monitoring model fitness and drift
- Planning delivery dependencies and the critical path
- Preparing analysis-ready datasets reproducibly
- 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 and tracing unfamiliar code
- 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 misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- 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
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- 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
- 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 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)
- Jira
- ServiceNow
- Datadog
- Microsoft Power BI
- Git / GitHub
- GitHub Copilot
- Azure DevOps
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonData Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics
About the role
Accountable for an engineering team's delivery quality, its change and incident practice and the development of its engineers. Decides priorities, approves changes the team makes and leads incident response for its services. Data and AI judgement sits in weighing reliability and data risk against delivery pressure and in governing how the team uses AI tools.
Also called Data Engineering Manager, Software Development Manager