Cloud & Infrastructure Engineer
Accountable for building, configuring, securing and recovering the cloud and on-premises infrastructure that data and AI services run on.
3tasks in this job
17skills it uses
6tools named
The skills this job leans on most
- 1Evaluating software architecture for data and AI systemsNeeded in 3 of 3 tasks
- 2Assessing data-service continuity and recoverabilityNeeded in 3 of 3 tasks
- 3Planning and validating controlled data migrationsNeeded in 2 of 3 tasks
- 4Writing maintainable code for analytical and data workNeeded in 1 of 3 tasks
- 5Enforcing and auditing data accessNeeded in 1 of 3 tasks
ToolsMicrosoft Azure (portal, Cost Management, Monitor)Microsoft ExcelServiceNowAmazon Web Services (console, Cost Explorer, CloudWatch)PythonKubernetesTerraformGitHub ActionsGrafanaDynatrace
All 3 tasks, where people get stuck and all 17 skills
Tasks
- Assess continuity and third-party dependency risk for a data or AI service
- Plan and demonstrate controlled data-service restoration
- Build and secure the infrastructure for a data or AI service as reviewed code
Where people get stuck
All skills
- Assessing change risk, collisions and go or no-go
- Coordinating accountable handoffs between teams
- Designing automated pipelines and processes that fail safely and recover
- Escalating unresolved risks with evidence and clear ownership
- Evaluating service performance and quality trade-offs
- Explaining technical work to accountable non-specialists
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Interpreting policy scope and delegated authority limits
- Mapping proposed process changes and control impacts
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- Repairing data defects reversibly under owner authority
- Running incident command: severity, timeline and status updates
- Testing changes to data services and business systems for business meaning and downstream impact
- Tracing data lineage upstream and downstream
- Writing verifiable requirements and acceptance criteria
Paths to start
Coming soonData Platform Architecture, Integration and EconomicsData Product Reliability: Release Gates, Service Levels and Incidents
5tasks in this job
80skills it uses
10tools named
The skills this job leans on most
- 1Assessing data-service continuity and recoverabilityNeeded in 5 of 5 tasks
- 2Evaluating software architecture for data and AI systemsNeeded in 4 of 5 tasks
- 3Planning and validating controlled data migrationsNeeded in 3 of 5 tasks
- 4Writing maintainable code for analytical and data workNeeded in 2 of 5 tasks
- 5Enforcing and auditing data accessNeeded in 1 of 5 tasks
ToolsMicrosoft Azure (portal, Cost Management, Monitor)Microsoft ExcelServiceNowAmazon Web Services (console, Cost Explorer, CloudWatch)PythonKubernetes+ 4 more
All 5 tasks, where people get stuck and all 80 skills
Tasks
- Evaluate platform performance and cost
- Assess continuity and third-party dependency risk for a data or AI service
- Plan and demonstrate controlled data-service restoration
- Evaluate and recommend a data, analytics or AI product or vendor
- Build and secure the infrastructure for a data or AI service as reviewed code
Where people get stuck
- Escalating unresolved risks with evidence and clear ownership
- Preserving incident evidence and timeline
All skills
- Assessing data-service continuity and recoverability
- Enforcing and auditing data access
- Evaluating software architecture for data and AI systems
- Writing maintainable code for analytical and data work
- 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 change risk, collisions and go or no-go
- Assessing data source credibility and fitness for a question
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Building data protection by design into data products and pipelines
- 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
- Comparing groups
- Comparing options against explicit decision criteria
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- 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 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
- 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
- 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
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- 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
- Mapping proposed process changes and control impacts
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Preparing change records with impact assessment and rollback
- 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 charts and dashboards correctly, including their state
- Reasoning about systems, feedback and second-order effects
- Recognising personal data and privacy duties
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Running incident command: severity, timeline and status updates
- 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
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
Paths to start
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonData Platform Architecture, Integration and EconomicsData Product Reliability: Release Gates, Service Levels and Incidents
7tasks in this job
101skills it uses
11tools named
The skills this job leans on most
- 1Assessing data-service continuity and recoverabilityNeeded in 6 of 7 tasks
- 2Planning and validating controlled data migrationsNeeded in 5 of 7 tasks
- 3Evaluating software architecture for data and AI systemsNeeded in 4 of 7 tasks
- 4Writing maintainable code for analytical and data workNeeded in 3 of 7 tasks
- 5Enforcing and auditing data accessNeeded in 2 of 7 tasks
ToolsMicrosoft Azure (portal, Cost Management, Monitor)GrafanaSplunkKubernetesMicrosoft ExcelServiceNow+ 5 more
All 7 tasks, where people get stuck and all 101 skills
Tasks
- Evaluate platform performance and cost
- Lead an incident through containment, restoration and communication
- Assess continuity and third-party dependency risk for a data or AI service
- Plan and demonstrate controlled data-service restoration
- Evaluate and recommend a data, analytics or AI product or vendor
- Build and secure the infrastructure for a data or AI service as reviewed code
- Set engineering standards for testing, change, data handling and AI-assisted code
Where people get stuck
- Evaluating software architecture for data and AI systems
- Escalating unresolved risks with evidence and clear ownership
- Assessing supplier dependency, data-handling and exit risk
All skills
- Assessing data-service continuity and recoverability
- Enforcing and auditing data access
- Evaluating software architecture for data and AI systems
- Writing maintainable code for analytical and data work
- 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 change risk, collisions and go or no-go
- Assessing data source credibility and fitness for a question
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- 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 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
- 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
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating responsible AI use risks
- Evaluating service performance and quality trade-offs
- Evaluating supplier total cost and value
- 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
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- 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
- Judging whether uncertainty changes the decision
- Making accountable decisions under uncertainty
- Mapping proposed process changes and control impacts
- Monitoring data quality and freshness in production
- 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 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
- Reviewing human- and AI-generated code changes for correctness and risk
- 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
- 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
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Azure (portal, Cost Management, Monitor)
- Grafana
- Splunk
- Kubernetes
- Microsoft Excel
- ServiceNow
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Purview
- Terraform
- Git / GitHub
- GitHub Actions
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
Skill Path
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonData Platform Architecture, Integration and Economics
About the role
Accountable for building, configuring, securing and recovering the cloud and on-premises infrastructure that data and AI services run on. Decides how infrastructure is defined as code and sized within cost and security limits and judges whether backups and recovery protect the data the business cannot lose. Differs from the Platform Engineer by owning the infrastructure layer rather than the shared platform services and delivery tooling built on it.
Also called Cloud Engineer, Infrastructure Engineer