Lead Engineer
Accountable for leading engineering work across a service or platform area and for the design of its larger changes.
4tasks in this job
47skills it uses
10tools named
The skills this job leans on most
- 1Writing maintainable code for analytical and data workNeeded in 3 of 4 tasks
- 2Reviewing human- and AI-generated code changes for correctness and riskNeeded in 3 of 4 tasks
- 3Testing changes to data services and business systems for business meaning and downstream impactNeeded in 3 of 4 tasks
ToolsDatadogAmazon Web Services (console, Cost Explorer, CloudWatch)ServiceNowJiraGit / GitHubGitLabDockerPostgreSQLClaudeAzure DevOps
All 4 tasks, where people get stuck and all 47 skills
Tasks
- Investigate application or API faults
- Prepare and verify a data-service release or migration for change approval
- 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
- Gating releases on evidence with rollback ready
- Calculating and verifying KPIs against their specification
- Planning and validating controlled data migrations
All skills
- Reviewing human- and AI-generated code changes for correctness and risk
- Testing changes to data services and business systems for business meaning and downstream impact
- Analysing queue behaviour, capacity and service constraints
- Assessing change risk, collisions and go or no-go
- Assessing data-service continuity and recoverability
- 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
- Communicating quantified uncertainty
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- Defining measurable outcomes and leading indicators
- Designing decision-oriented research with customers, users or employees
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- 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
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- 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
- Recognising and containing suspected personal data breaches
- 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
- 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 verifiable requirements and acceptance criteria
Paths to start
Coming soonEngineering Foundations for Data and AI WorkAnalytics Engineering: Tested, Version-controlled Transformation
9tasks in this job
92skills it uses
13tools named
The skills this job leans on most
- 1Reviewing human- and AI-generated code changes for correctness and riskNeeded in 7 of 9 tasks
- 2Gating releases on evidence with rollback readyNeeded in 7 of 9 tasks
- 3Testing changes to data services and business systems for business meaning and downstream impactNeeded in 6 of 9 tasks
- 4Writing maintainable code for analytical and data workNeeded in 5 of 9 tasks
- 5Calculating and verifying KPIs against their specificationCore to this role
ToolsDatadogVisual Studio CodeJiraAzure DevOpsServiceNowKubernetes+ 7 more
All 9 tasks, where people get stuck and all 92 skills
Tasks
- Investigate application or API faults
- 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
- 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
- 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
- Assessing change risk, collisions and go or no-go
All skills
- Gating releases on evidence with rollback ready
- Reviewing human- and AI-generated code changes for correctness and risk
- Testing changes to data services and business systems for business meaning and downstream impact
- Writing maintainable code for analytical and data work
- 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 change risk, collisions and go or no-go
- Assessing data-service continuity and recoverability
- 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
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Converting demand into capacity requirements
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- 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 enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing relational schemas: keys, constraints and normalisation
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- 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 product outcomes and metric integrity
- 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
- Forecasting operational demand at interval and site grain
- 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 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
- 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
- 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
- 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
- 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
- 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
- Datadog
- Visual Studio Code
- Jira
- Azure DevOps
- ServiceNow
- Kubernetes
- Grafana
- Git / GitHub
- GitLab
- PostgreSQL
- Terraform
- GitHub Copilot
- Docker
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonEngineering Foundations for Data and AI WorkData Product Reliability: Release Gates, Service Levels and Incidents
11tasks in this job
126skills it uses
13tools named
The skills this job leans on most
- 1Reviewing human- and AI-generated code changes for correctness and riskNeeded in 9 of 11 tasks
- 2Gating releases on evidence with rollback readyNeeded in 9 of 11 tasks
- 3Planning and validating controlled data migrationsNeeded in 7 of 11 tasks
- 4Writing maintainable code for analytical and data workNeeded in 6 of 11 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 6 of 11 tasks
ToolsDatadogAmazon Web Services (console, Cost Explorer, CloudWatch)Microsoft Azure (portal, Cost Management, Monitor)JiraServiceNowKubernetes+ 7 more
All 11 tasks, where people get stuck and all 126 skills
Tasks
- Investigate application or API faults
- 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
- Assess continuity and third-party dependency risk for a data or AI service
- Deliver a tested software or API change to a data or AI service
- 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
- 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
- Assessing change risk, collisions and go or no-go
All skills
- Gating releases on evidence with rollback ready
- Planning and validating controlled data migrations
- Reviewing human- and AI-generated code changes for correctness and risk
- Testing changes to data services and business systems for business meaning and downstream impact
- 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
- 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 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
- Debugging data and application services from evidence
- 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 relational schemas: keys, constraints and normalisation
- Designing semantic models for governed analytics
- Diagnosing and remedying query and database performance
- 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
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- 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
- 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
- Forecasting operational demand at interval and site grain
- 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
- 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 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
- 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
- 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
- Standardising and cleaning data under agreed rules
- 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
- 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
- Datadog
- Amazon Web Services (console, Cost Explorer, CloudWatch)
- Microsoft Azure (portal, Cost Management, Monitor)
- Jira
- ServiceNow
- Kubernetes
- Grafana
- Git / GitHub
- GitLab
- Terraform
- GitHub Copilot
- PostgreSQL
- Azure DevOps
Paths to start
Coming soonEngineering Foundations for Data and AI WorkData Product Reliability: Release Gates, Service Levels and IncidentsAnalytics Engineering: Tested, Version-controlled Transformation
About the role
Accountable for leading engineering work across a service or platform area and for the design of its larger changes. Sets engineering standards and reviews others' work without line management across software, platform, cloud and database specialisms. Data and AI judgement sits in data-handling conventions and how AI-assisted code is checked.
Also called Tech Lead, Lead Software Engineer, Lead Platform Engineer, Lead Cloud & Infrastructure Engineer, Lead Database Administrator