Roles Marketing & Communications
Marketing Operations Manager
Accountable for a marketing operations team and for the reliability of marketing systems, data flows and consent handling.
8tasks in this job
111skills it uses
10tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 5 of 8 tasks
- 2Analysing staged funnels: conversion, time in stage and slippageNeeded in 3 of 8 tasks
- 3Reconstructing a record's state at a point in time from history and audit trailsNeeded in 3 of 8 tasks
- 4Diagnosing duplicate recordsNeeded in 2 of 8 tasks
- 5Qualifying sales opportunities with traceable evidenceCore to this role
ToolsSalesforceSalesforce Marketing CloudHubSpotMicrosoft Power BIAdobe Marketo EngageOneTrustMicrosoft ExcelBrazeMicrosoft Dynamics 365 Sales and Customer InsightsJira
All 8 tasks, where people get stuck and all 111 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Produce an accountable operating budget
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Configure, test and release a change to a business system of record under change control
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Design and calibrate lead scoring and routing rules for the marketing-to-sales handoff
Where people get stuck
- Evaluating fitness and full cost of AI-assisted work
- Comparing options against explicit decision criteria
- Appraising options on a common incremental basis against a do-nothing baseline
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Designing and validating digital analytics event measurement
- Diagnosing duplicate records
- Reconstructing a record's state at a point in time from history and audit trails
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- 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 driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- 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
- Estimating and challenging causal effects
- Evaluating ML generalisation and fitness
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- 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
- Managing master data identities and exceptions
- 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 change records with impact assessment and rollback
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Reading business metrics
- 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 personal data and privacy duties
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- 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
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 soonProduct and Digital Behaviour Analytics
11tasks in this job
140skills it uses
11tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 7 of 11 tasks
- 2Diagnosing duplicate recordsNeeded in 4 of 11 tasks
- 3Analysing staged funnels: conversion, time in stage and slippageNeeded in 3 of 11 tasks
- 4Evaluating fitness and full cost of AI-assisted workNeeded in 1 of 11 tasks
- 5Comparing options against explicit decision criteriaNeeded in 1 of 11 tasks
ToolsSalesforceSalesforce Marketing CloudHubSpotMicrosoft Power BIAdobe Marketo EngageOneTrust+ 5 more
All 11 tasks, where people get stuck and all 140 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Produce an accountable operating budget
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Assign data ownership and stewardship and decide a domain's data standards
- Evaluate and recommend a data, analytics or AI product or vendor
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Configure, test and release a change to a business system of record under change control
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Design and calibrate lead scoring and routing rules for the marketing-to-sales handoff
Where people get stuck
- Assigning and operating data ownership and stewardship
- Designing enforceable data governance policies
- Identifying and prioritising critical data elements
All skills
- Analysing staged funnels: conversion, time in stage and slippage
- Comparing options against explicit decision criteria
- Designing and validating digital analytics event measurement
- Diagnosing duplicate records
- Evaluating fitness and full cost of AI-assisted work
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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 driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- 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 and challenging causal effects
- Evaluating ML generalisation and fitness
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- 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 change records with impact assessment and rollback
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- 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
- 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
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- 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
- Salesforce Marketing Cloud
- HubSpot
- Microsoft Power BI
- Adobe Marketo Engage
- OneTrust
- Microsoft Excel
- Braze
- Microsoft Dynamics 365 Sales and Customer Insights
- Coupa
- Jira
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
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 soonProduct and Digital Behaviour Analytics
13tasks in this job
142skills it uses
11tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 7 of 13 tasks
- 2Assigning and operating data ownership and stewardshipNeeded in 5 of 13 tasks
- 3Diagnosing duplicate recordsNeeded in 4 of 13 tasks
- 4Designing enforceable data governance policiesNeeded in 4 of 13 tasks
- 5Reconstructing a record's state at a point in time from history and audit trailsNeeded in 3 of 13 tasks
ToolsSalesforceSalesforce Marketing CloudHubSpotMicrosoft Power BIAdobe Marketo EngageOneTrust+ 5 more
All 13 tasks, where people get stuck and all 142 skills
Tasks
- Diagnose CRM record and lifecycle integrity across marketing-to-sales handoffs, including consent
- Define and repair a tracking plan and event taxonomy and restore reporting reliability
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Triage a suspected data security or personal data breach and preserve evidence
- Produce an accountable operating budget
- Plan and evaluate a consent-aware CRM lifecycle intervention
- 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
- Define master data match and survivorship rules and recommend golden-record changes for the data owner's approval
- Configure, test and release a change to a business system of record under change control
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Design and calibrate lead scoring and routing rules for the marketing-to-sales handoff
Where people get stuck
- Identifying and prioritising critical data elements
- Resolving consent and suppression status across systems
All skills
- Assigning and operating data ownership and stewardship
- Designing and validating digital analytics event measurement
- Designing enforceable data governance policies
- Diagnosing duplicate records
- Reconstructing a record's state at a point in time from history and audit trails
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing pay equity with raw and adjusted pay gaps
- Analysing staged funnels: conversion, time in stage and slippage
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing change risk, collisions and go or no-go
- 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
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Calculating and verifying KPIs against their specification
- Challenging probability-weighted sales forecasts
- Choosing an analytical method to fit the question and data
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Constructing a controlled cash-flow plan
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- Defining measurable outcomes and leading indicators
- Designing and comparing decision scenarios
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Designing decision rights and dispute routes for data and AI work
- Designing experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing data quality exceptions
- Diagnosing digital funnel conversion from event data
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- 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 and challenging causal effects
- Evaluating ML generalisation and fitness
- Evaluating fitness and full cost of AI-assisted work
- Evaluating learning transfer and performance outcomes
- Evaluating responsible AI use risks
- Evaluating supplier total cost and value
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Facilitating evidence-led retrospectives and corrective actions
- Forecasting business trends and turning points for planning
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting supplier contract duties, terms and exposure
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- 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
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- 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 change records with impact assessment and rollback
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- 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
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- Setting and reviewing action thresholds for scores and model predictions
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying AI transparency and explanation duties to affected people
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying reports and dashboards before and after release
- Weighing conflicting evidence by design strength
- 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
- Salesforce Marketing Cloud
- HubSpot
- Microsoft Power BI
- Adobe Marketo Engage
- OneTrust
- Microsoft Excel
- Braze
- Microsoft Dynamics 365 Sales and Customer Insights
- Coupa
- Jira
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Skill Path
Data Governance, Catalogues and Lineage
Govern definitions, stewardship, lineage and enforceable policy.
- Modules
- 5 open
- Practice data
- Raket Parts
About the role
Accountable for a marketing operations team and for the reliability of marketing systems, data flows and consent handling. Sets the team's priorities and allocates its capacity across the stack. Data judgement sits in marketing data rules and in deciding which tools and integrations the team can trust.
Also called CRM Manager, Lifecycle Marketing Manager