Explore Tasks
Set engineering standards for testing, change, data handling and AI-assisted code
Engineering teams receive a documented, adopted standard for testing, change gates, data handling at interfaces and review of AI-assisted code, with how compliance is checked.
4jobs do it
9jobs help with it
50skills it needs
- Lead Analytics Engineer
- Lead Data Engineer
- Lead AI & ML Engineer
- Lead Engineer
- Analytics Engineer
- Data Engineer
- Machine Learning Engineer
- AI Engineer
- Software Engineer
- Platform Engineer
- Cloud & Infrastructure Engineer
- Engineering Manager
- Head of Engineering
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
- Classifying AI uses against regulatory obligations
- Governing shared data definitions
- Judging whether and how to use AI for a task
- Applying data classification and handling rules
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Mapping stakeholders' decisions, constraints and needs
- Working through stakeholder conflict over evidence and responsibility
- Identifying and prioritising critical data elements
- Designing decision rights and dispute routes for data and AI work
- Identifying and quantifying decision downside and exposure
- Making accountable decisions under uncertainty
- 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
- Evaluating and improving chart accessibility
- Controlling disclosure risk in shared data and outputs
- Influencing change without formal authority
- Designing implementable change plans
- Writing testable work standards and review checklists
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