Head of Revenue Operations
Accountable for the revenue operations discipline and for the integrity of the revenue process from pipeline to billed revenue.
2tasks in this job
50skills it uses
6tools named
The skills this job leans on most
- 1Diagnosing duplicate recordsNeeded in 1 of 2 tasks
- 2Validating records by matching, recomputation and end-to-end tracingNeeded in 1 of 2 tasks
- 3Reconciling finance subledgers and close recordsNeeded in 1 of 2 tasks
- 4Reconstructing a record's state at a point in time from history and audit trailsNeeded in 1 of 2 tasks
- 5Resolving consent and suppression status across systemsNeeded in 1 of 2 tasks
ToolsSalesforceHubSpotMicrosoft Power BIMicrosoft PowerPointOracle NetSuiteSAP S/4HANAMicrosoft ExcelMicrosoft SharePointClari
All 2 tasks, where people get stuck and all 50 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
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
- Diagnosing duplicate records
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Validating records by matching, recomputation and end-to-end tracing
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing periods consistently
- Constructing and challenging customer segments
- Designing and validating digital analytics event measurement
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Explaining how business events flow into accounting records
- Giving specific developmental feedback on data and AI work
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Locating and acting on personal data for individual rights requests
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Profiling data
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
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
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
9tasks in this job
118skills it uses
9tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 5 of 9 tasks
- 2Calculating and verifying KPIs against their specificationNeeded in 3 of 9 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 3 of 9 tasks
- 4Diagnosing duplicate recordsNeeded in 2 of 9 tasks
- 5Validating records by matching, recomputation and end-to-end tracingNeeded in 2 of 9 tasks
ToolsSalesforceHubSpotMicrosoft Power BIMicrosoft PowerPointOracle NetSuiteSAP S/4HANA+ 3 more
All 9 tasks, where people get stuck and all 118 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and govern a KPI metric contract
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Deliver and sustain a trusted BI report or dashboard
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Evaluate and recommend a data, analytics or AI product or vendor
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Design and calibrate lead scoring and routing rules for the marketing-to-sales handoff
Where people get stuck
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving consent and suppression status across systems
All skills
- Calculating and verifying KPIs against their specification
- Diagnosing duplicate records
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Validating records by matching, recomputation and end-to-end tracing
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- 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 evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
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
Skill Path
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Customer, Revenue and Sales Analytics
Analyse pipeline, forecast quality, segments and lifetime value from CRM and billing data.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI Products
10tasks in this job
127skills it uses
10tools named
The skills this job leans on most
- 1Governing shared data definitionsNeeded in 5 of 10 tasks
- 2Calculating and verifying KPIs against their specificationNeeded in 3 of 10 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 3 of 10 tasks
ToolsSalesforceHubSpotMicrosoft Power BIMicrosoft PowerPointOracle NetSuiteSAP S/4HANA+ 4 more
All 10 tasks, where people get stuck and all 127 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and govern a KPI metric contract
- Reconcile booked, fulfilled, billed and recognised revenue across CRM, ERP and finance records
- Deliver and sustain a trusted BI report or dashboard
- Maintain a data and AI risk register and record risk treatment and acceptance
- Assign data ownership and stewardship and decide a domain's data standards
- Evaluate and recommend a data, analytics or AI product or vendor
- Assess forecast accuracy and bias and recommend which forecast methods to rely on
- Analyse a current business process from event and operational data and define the future-state process
- Design and calibrate lead scoring and routing rules for the marketing-to-sales handoff
Where people get stuck
- Calculating and verifying KPIs against their specification
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Calculating and verifying KPIs against their specification
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing staged funnels: conversion, time in stage and slippage
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- 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
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing groups
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing duplicate records
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- 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
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Maintaining requirements traceability and change control
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- 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 evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Salesforce
- HubSpot
- Microsoft Power BI
- Microsoft PowerPoint
- Oracle NetSuite
- SAP S/4HANA
- Microsoft Excel
- Microsoft SharePoint
- Clari
- Jira
Paths to start
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
KPI, Variance and Performance Analysis
Explain performance against plan with variance decomposition and driver trees.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI Products
About the role
Accountable for the revenue operations discipline and for the integrity of the revenue process from pipeline to billed revenue. Heads the revenue operations managers and decides CRM rules, tooling investment and the revenue reporting standards the business relies on. Data judgement sits in deciding which bookings and pipeline data are reliable enough to forecast and reconcile against.
Also called Head of RevOps, Head of Sales Operations