4tasks in this job
31skills it uses
9tools named
The skills this job leans on most
- 1Designing and versioning data contracts for consumersNeeded in 3 of 4 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 2 of 4 tasks
- 3Evaluating product outcomes and metric integrityNeeded in 1 of 4 tasks
ToolsCollibraMicrosoft ExcelMicrosoft PurviewMicrosoft Power BISQL (query language)DatabricksdbtSAP S/4HANASalesforce
All 4 tasks, where people get stuck and all 31 skills
Tasks
- Define and govern a KPI metric contract
- Elicit, analyse and manage requirements for a data, analytics or system change
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Defining measurable outcomes and leading indicators
- Applying data classification and handling rules
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Communicating quantified uncertainty
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing operational handoffs and flow
- Eliciting needs through interviews, workshops and observation
- Escalating unresolved risks with evidence and clear ownership
- Identifying keys and testing referential integrity
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Prioritising work by evidenced value, cost, risk and confidence
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Writing data quality rules
- Writing verifiable requirements and acceptance criteria
Paths to start
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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
6tasks in this job
58skills it uses
12tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 5 of 6 tasks
- 2Designing and versioning data contracts for consumersNeeded in 5 of 6 tasks
- 3Calculating and verifying KPIs against their specificationNeeded in 4 of 6 tasks
- 4Specifying BI measure semantics: additivity, aggregation and timeNeeded in 4 of 6 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 6 tasks
ToolsCollibraMicrosoft PurviewSQL (query language)DatabricksSnowflakedbt+ 6 more
All 6 tasks, where people get stuck and all 58 skills
Tasks
- Define and govern a KPI metric contract
- Assess data quality and fitness for a stated use
- Design and build a tested, governed analytical data model
- Elicit, analyse and manage requirements for a data, analytics or system change
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
- Tracing data lineage upstream and downstream
- Testing changes to data services and business systems for business meaning and downstream impact
All skills
- Calculating and verifying KPIs against their specification
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Communicating quantified uncertainty
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing operational handoffs and flow
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating product outcomes and metric integrity
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing verifiable requirements and acceptance criteria
All tools
- Collibra
- Microsoft Purview
- SQL (query language)
- Databricks
- Snowflake
- dbt
- Jira
- Microsoft Excel
- Azure DevOps
- Microsoft Power BI
- SAP S/4HANA
- Salesforce
Paths to start
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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI Products
5tasks in this job
67skills it uses
10tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 5 of 5 tasks
- 2Designing and versioning data contracts for consumersNeeded in 5 of 5 tasks
- 3Tracing data lineage upstream and downstreamNeeded in 4 of 5 tasks
- 4Calculating and verifying KPIs against their specificationNeeded in 4 of 5 tasks
- 5Specifying BI measure semantics: additivity, aggregation and timeNeeded in 4 of 5 tasks
ToolsCollibraMicrosoft PurviewSQL (query language)SnowflakeDatabricksdbt+ 4 more
All 5 tasks, where people get stuck and all 67 skills
Tasks
- Define and govern a KPI metric contract
- Assess data quality and fitness for a stated use
- Design and build a tested, governed analytical data model
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
All skills
- Calculating and verifying KPIs against their specification
- Designing and versioning data contracts for consumers
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Tracing data lineage upstream and downstream
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Communicating quantified uncertainty
- Defining measurable outcomes and leading indicators
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating product outcomes and metric integrity
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping customer journeys and evidence gaps
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing verifiable requirements and acceptance criteria
All tools
- Collibra
- Microsoft Purview
- SQL (query language)
- Snowflake
- Databricks
- dbt
- Jira
- Azure DevOps
- SAP S/4HANA
- Salesforce
Paths to start
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
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonManaging Data and AI Products
About the role
Accountable for the value and trusted delivery of data products that other teams use. Decides what each data product must guarantee to its consumers and writes those guarantees into data contracts. Analytics engineers build the models while the Data Product Manager owns what they promise and to whom.
Also called Data Product Owner