Explore Tasks
Build and secure the infrastructure for a data or AI service as reviewed code
A service owner receives infrastructure defined as reviewed code, sized within cost limits, meeting the security baseline and monitored, ready for the service to run on.
2jobs do it
2jobs help with it
35skills it needs
Does itHelps with it
- Understanding and interrogating data and application APIs
- Writing maintainable code for analytical and data work
- Debugging data and application services from evidence
- Reviewing human- and AI-generated code changes for correctness and risk
- Building bounded, secure data-service APIs
- Gating releases on evidence with rollback ready
- Tracing data lineage upstream and downstream
- Repairing data defects reversibly under owner authority
- Diagnosing and remedying query and database performance
- Identifying keys and testing referential integrity
- Designing relational schemas: keys, constraints and normalisation
- Testing changes to data services and business systems for business meaning and downstream impact
- Enforcing and auditing data access
- Planning and validating controlled data migrations
- Writing verifiable requirements and acceptance criteria
- Preparing change records with impact assessment and rollback
- Building data protection by design into data products and pipelines
- Reading and tracing unfamiliar code
- 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
- Designing automated pipelines and processes that fail safely and recover
- Evaluating software architecture for data and AI systems
- 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
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
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
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
Coming soonData Product Reliability: Release Gates, Service Levels and IncidentsData Platform Architecture, Integration and Economics