Explore Tasks
Build a tested batch or streaming data pipeline
Consumers receive a pipeline with validated ingestion, tests, monitoring and a documented recovery procedure, reviewed by a Platform Engineer.
1job does it
2jobs help with it
33skills it needs
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
- 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
- Establishing and declaring data grain
- Standardising and cleaning data under agreed rules
- Tracing data lineage upstream and downstream
- Monitoring data quality and freshness in production
- Building decision-focused reports and dashboards
- Designing semantic models for governed analytics
- Identifying keys and testing referential integrity
- Designing relational schemas: keys, constraints and normalisation
- Designing dimensional models with explicit grain
- 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
- 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
- Evaluating service performance and quality trade-offs
Paths that teach the skills it needs
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 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
Coming soonAnalytics Engineering: Tested, Version-controlled TransformationData Product Reliability: Release Gates, Service Levels and Incidents