Platform Engineer
Accountable for safe change and agreed reliability objectives on shared data and AI platform services.
2tasks in this job
14skills it uses
8tools named
The skills this job leans on most
- 1Gating releases on evidence with rollback readyNeeded in 2 of 2 tasks
- 2Planning and validating controlled data migrationsNeeded in 1 of 2 tasks
- 3Preparing change records with impact assessment and rollbackNeeded in 1 of 2 tasks
- 4Assessing change risk, collisions and go or no-goNeeded in 1 of 2 tasks
ToolsGitHub ActionsServiceNowGit / GitHubTerraformPythonGrafanaPrometheusDatadog
All 2 tasks, where people get stuck and all 14 skills
Tasks
- Prepare and verify a data-service release or migration for change approval
- Define, monitor and review data-service reliability objectives
Where people get stuck
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Monitoring data quality and freshness in production
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
- Coordinating accountable handoffs between teams
- Escalating unresolved risks with evidence and clear ownership
- Explaining technical work to accountable non-specialists
- Identifying keys and testing referential integrity
- Interpreting policy scope and delegated authority limits
- Mapping proposed process changes and control impacts
- 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 Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics
7tasks in this job
67skills it uses
13tools named
The skills this job leans on most
- 1Gating releases on evidence with rollback readyNeeded in 6 of 7 tasks
- 2Monitoring data quality and freshness in productionNeeded in 5 of 7 tasks
- 3Setting and reviewing reliability objectives and error budgets for data and AI servicesNeeded in 3 of 7 tasks
- 4Planning and validating controlled data migrationsNeeded in 3 of 7 tasks
- 5Preparing change records with impact assessment and rollbackNeeded in 2 of 7 tasks
ToolsDatabricksServiceNowGitHub ActionsTerraformSplunkDynatrace+ 7 more
All 7 tasks, where people get stuck and all 67 skills
Tasks
- Diagnose a data pipeline failure and recover safely
- 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
- Apply an approved data access grant or revocation and prove its effect
- Build a tested batch or streaming data pipeline
- 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
- Running incident command: severity, timeline and status updates
- Preserving incident evidence and timeline
All skills
- Gating releases on evidence with rollback ready
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Analysing error patterns and failure modes in models and AI systems
- Applying data classification and handling rules
- Assessing change risk, collisions and go or no-go
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- 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
- Constructing clear and honest charts
- Coordinating accountable handoffs between teams
- Designing automated pipelines and processes that fail safely and recover
- Designing enforceable data governance policies
- Designing role-based access entitlement models
- Designing semantic models for governed analytics
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- 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 service performance and quality trade-offs
- Evaluating software architecture for data and AI systems
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting correlation without inferring cause
- 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 uncertainty changes the decision
- Mapping proposed process changes and control impacts
- Monitoring and testing data control effectiveness
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- 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
- 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
- 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
- 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 maintainable code for analytical and data work
- Writing verifiable requirements and acceptance criteria
All tools
- Databricks
- ServiceNow
- GitHub Actions
- Terraform
- Splunk
- Dynatrace
- Grafana
- Prometheus
- Datadog
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Git / GitHub
- Kubernetes
- Docker
Paths to start
Coming soonData Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and EconomicsPython for Data Analysis
9tasks in this job
93skills it uses
14tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 9 of 9 tasks
- 2Gating releases on evidence with rollback readyNeeded in 8 of 9 tasks
- 3Monitoring data quality and freshness in productionNeeded in 6 of 9 tasks
- 4Planning and validating controlled data migrationsNeeded in 5 of 9 tasks
- 5Setting and reviewing reliability objectives and error budgets for data and AI servicesNeeded in 4 of 9 tasks
ToolsDatabricksAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)Git / GitHubServiceNowGitHub Actions+ 8 more
All 9 tasks, where people get stuck and all 93 skills
Tasks
- Diagnose a data pipeline failure and recover safely
- Evaluate platform performance and cost
- 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
- Apply an approved data access grant or revocation and prove its effect
- Build a tested batch or streaming data pipeline
- 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
- Gating releases on evidence with rollback ready
- Escalating unresolved risks with evidence and clear ownership
- Assessing change risk, collisions and go or no-go
All skills
- Escalating unresolved risks with evidence and clear ownership
- Gating releases on evidence with rollback ready
- Monitoring data quality and freshness in production
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- 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 lawful basis, purpose compatibility and minimisation for data reuse
- 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
- 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
- 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 interpreting hypothesis tests
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing role-based access entitlement models
- 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
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- 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
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Monitoring and testing data control effectiveness
- 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
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- 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
- 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
- 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
- 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 maintainable code for analytical and data work
- Writing verifiable requirements and acceptance criteria
All tools
- Databricks
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Azure (portal, Cost Management, Monitor)
- Git / GitHub
- ServiceNow
- GitHub Actions
- Dynatrace
- Splunk
- Grafana
- Datadog
- Prometheus
- Terraform
- Kubernetes
- Docker
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
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 Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics
About the role
Accountable for safe change and agreed reliability objectives on shared data and AI platform services. Decides whether a change passes its gates and what reliability objective a service should meet. Differs from the Cloud & Infrastructure Engineer by running shared platform services rather than the underlying cloud infrastructure.
Also called DevOps Engineer, Site Reliability Engineer, Data Platform Engineer