Explore Tasks
Design and build a tested, governed analytical data model
Consumers receive a documented, version-controlled analytical or semantic model with explicit grain and keys, tested transformations and an agreed metric contract implemented, reviewed by the Data Architect and owned by the Data Product Manager.
2jobs do it
6jobs help with it
40skills it needs
- BI Developer
- Analytics Engineer
- Analytics Lead
- Lead Analytics Engineer
- Data Architect
- Head of Data Architecture
- Product Analyst
- Data Product Manager
Does itHelps with it
- 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
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Establishing and declaring data grain
- Reading business metrics
- Governing shared data definitions
- Writing data quality rules
- Reconciling conflicting reports
- Tracing data lineage upstream and downstream
- Calculating and verifying KPIs against their specification
- Specifying decision-useful reports
- Specifying BI measure semantics: additivity, aggregation and time
- Designing semantic models for governed analytics
- Identifying keys and testing referential integrity
- Designing dimensional models with explicit grain
- Designing and versioning data contracts for consumers
- Assigning and operating data ownership and stewardship
- Mapping stakeholders' decisions, constraints and needs
- Writing maintainable code for analytical and data work
- Gating releases on evidence with rollback ready
- Designing automated pipelines and processes that fail safely and recover
- Standardising and cleaning data under agreed rules
- Monitoring data quality and freshness in production
- Building decision-focused reports and dashboards
- Designing relational schemas: keys, constraints and normalisation
- Testing changes to data services and business systems for business meaning and downstream impact
- Designing and validating batch and streaming ingestion
- Engineering production data transformations
- Recovering data pipelines safely
- Building data protection by design into data products and pipelines
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 soonAnalytics Engineering: Tested, Version-controlled Transformation