Explore Tasks
Evaluate platform performance and cost
The platform owner receives a cost and performance analysis with workload attribution and optimisation options.
3jobs do it
6jobs help with it
69skills it needs
- Lead Data Engineer
- Platform Engineer
- Cloud & Infrastructure Engineer
- Data Engineer
- Data Architect
- Head of Data Architecture
- Lead Engineer
- Engineering Manager
- Head of Engineering
Does itHelps with it
- Evaluating software architecture for data and AI systems
- Classifying AI uses against regulatory obligations
- Writing evidence-led findings and auditable recommendations with their limits
- Designing semantic models for governed analytics
- Designing relational schemas: keys, constraints and normalisation
- Designing dimensional models with explicit grain
- Explaining data and AI platform architecture patterns and building blocks
- Planning and validating controlled data migrations
- Recognising personal data and privacy duties
- Applying data classification and handling rules
- Writing verifiable requirements and acceptance criteria
- Comparing options against explicit decision criteria
- Making accountable decisions under uncertainty
- Assessing supplier dependency, data-handling and exit risk
- Building data protection by design into data products and pipelines
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Writing maintainable code for analytical and data work
- Fitting, checking and interpreting inferential regression models
- Estimating quantities and differences with intervals
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Calculating and interpreting percentages, rates and ratios
- Interpreting averages, weighted measures and mix effects
- Judging whether a change exceeds normal variation
- Formulating answerable data questions
- Assessing data source credibility and fitness for a question
- Profiling data
- Standardising and cleaning data under agreed rules
- Preparing analysis-ready datasets reproducibly
- Choosing and justifying missing-data treatments
- Describing distributions with summaries and plots
- Comparing groups
- Exploring data to find patterns that survive validation
- Investigating anomalies
- Designing and interpreting hypothesis tests
- Reading charts and dashboards correctly, including their state
- Selecting charts for evidence and decisions
- Defining the analysis population: filters, nulls, dates and time zones
- Analysing event sequences, states and durations
- Diagnosing incorrect query and calculation results
- Decomposing one's own tasks for AI assistance
- Training reproducible machine learning models
- Eliciting the decision behind an analytical request
- Designing efficient and reproducible investigations
- Choosing an analytical method to fit the question and data
- Gating releases on evidence with rollback ready
- Designing automated pipelines and processes that fail safely and recover
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Assessing data-service continuity and recoverability
- Monitoring data quality and freshness in production
- Recovering data pipelines safely
- Evaluating service performance and quality trade-offs
- Identifying and quantifying decision downside and exposure
- Revising prior decisions when evidence changes
- Reasoning about systems, feedback and second-order effects
- Facilitating group decisions using disputed evidence
- Rating risks against appetite and choosing proportionate treatment
- Recognising and resolving ethical conflicts in data and AI work
Paths that teach the skills it needs
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
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
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFraming Questions and Sourcing Evidence for Investigations