Explore Tasks
Define and repair a tracking plan and event taxonomy and restore reporting reliability
A marketing or product team receives corrected tracking conventions (event plan, naming, UTM and consent conditions) and a reconciled report showing that the restored tracking matches source records.
3jobs do it
4jobs help with it
48skills it needs
- Marketing Analyst
- Marketing Operations Lead
- Product Analyst
- Marketing Executive
- Marketing Operations Manager
- Product Manager
- Head of Marketing Operations
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
- Designing and validating digital analytics event measurement
- Reading data tables: rows, fields, types and units
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Reading business metrics
- Comparing periods consistently
- Assessing data source credibility and fitness for a question
- Diagnosing data quality exceptions
- Validating records by matching, recomputation and end-to-end tracing
- Reconciling conflicting reports
- Tracing data lineage upstream and downstream
- Calculating and verifying KPIs against their specification
- Assigning and operating data ownership and stewardship
- Reconciling finance subledgers and close records
- Investigating procurement spend leakage and duplicate commitments
- Explaining how business events flow into accounting records
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Governing shared data definitions
- Writing data quality rules
- 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
- Mapping stakeholders' decisions, constraints and needs
- Profiling data
- Standardising and cleaning data under agreed rules
- Diagnosing duplicate records
- Repairing data defects reversibly under owner authority
- Monitoring data quality and freshness in production
- Applying data classification and handling rules
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Designing enforceable data governance policies
- Managing master data identities and exceptions
- Influencing change without formal authority
Paths that teach the skills it needs
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
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