Roles Marketing & Communications
Marketing Operations Lead
Accountable for the marketing technology stack, data flows, reporting reliability and campaign to CRM handoffs.
5tasks in this job
24skills it uses
12tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 5 of 5 tasks
- 2Diagnosing data quality exceptionsNeeded in 4 of 5 tasks
- 3Writing data quality rulesNeeded in 4 of 5 tasks
- 4Repairing data defects reversibly under owner authorityNeeded in 4 of 5 tasks
- 5Diagnosing duplicate recordsNeeded in 3 of 5 tasks
ToolsHubSpotSalesforceSalesforce Marketing CloudSalesforce Marketing Cloud Account Engagement (Pardot)Lime CRMAdobe Marketo EngageMicrosoft Dynamics 365 Sales and Customer InsightsGoogle Tag ManagerTwilio SegmentVoyadoMicrosoft ExcelJira
All 5 tasks, where people get stuck and all 24 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
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Configure, test and release a change to a business system of record under change control
Where people get stuck
- Analysing staged funnels: conversion, time in stage and slippage
- Qualifying sales opportunities with traceable evidence
- Designing and weighting rule-based scoring models
All skills
- Designing and validating digital analytics event measurement
- Diagnosing data quality exceptions
- Repairing data defects reversibly under owner authority
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Calculating and verifying KPIs against their specification
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing periods consistently
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Explaining how business events flow into accounting records
- Identifying keys and testing referential integrity
- Interpreting correlation without inferring cause
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Preparing change records with impact assessment and rollback
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Testing changes to data services and business systems for business meaning and downstream impact
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- 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
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
11tasks in this job
93skills it uses
15tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 8 of 11 tasks
- 2Writing data quality rulesNeeded in 8 of 11 tasks
- 3Diagnosing data quality exceptionsNeeded in 6 of 11 tasks
- 4Repairing data defects reversibly under owner authorityNeeded in 6 of 11 tasks
- 5Diagnosing duplicate recordsNeeded in 5 of 11 tasks
ToolsHubSpotSalesforceSalesforce Marketing CloudSalesforce Marketing Cloud Account Engagement (Pardot)Salesforce Data LoaderLime CRM+ 9 more
All 11 tasks, where people get stuck and all 93 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
- Assess data quality and fitness for a stated use
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- Plan and evaluate a consent-aware CRM lifecycle intervention
- Evaluate and recommend a data, analytics or AI product or vendor
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- 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
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Controlling disclosure risk in shared data and outputs
- Interpreting policy scope and delegated authority limits
All skills
- Designing and validating digital analytics event measurement
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Repairing data defects reversibly under owner authority
- Writing data quality rules
- Analysing staged funnels: conversion, time in stage and slippage
- 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
- 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
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing efficient and reproducible investigations
- 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 digital funnel conversion from event data
- Diagnosing operational handoffs and flow
- 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
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- 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
- 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
- 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
- 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, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying benefits realisation and attribution
- Weighing conflicting evidence by design strength
- Working through stakeholder conflict over evidence and responsibility
- Writing verifiable requirements and acceptance criteria
All tools
- HubSpot
- Salesforce
- Salesforce Marketing Cloud
- Salesforce Marketing Cloud Account Engagement (Pardot)
- Salesforce Data Loader
- Lime CRM
- Adobe Marketo Engage
- Microsoft Dynamics 365 Sales and Customer Insights
- Google Tag Manager
- Twilio Segment
- Microsoft Excel
- OneTrust
- Voyado
- Jira
- Zapier
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
12tasks in this job
103skills it uses
15tools named
The skills this job leans on most
- 1Designing and validating digital analytics event measurementNeeded in 9 of 12 tasks
- 2Writing data quality rulesNeeded in 9 of 12 tasks
- 3Diagnosing data quality exceptionsNeeded in 7 of 12 tasks
- 4Repairing data defects reversibly under owner authorityNeeded in 7 of 12 tasks
- 5Diagnosing duplicate recordsNeeded in 6 of 12 tasks
ToolsHubSpotSalesforceSalesforce Marketing CloudSalesforce Marketing Cloud Account Engagement (Pardot)Salesforce Data LoaderLime CRM+ 9 more
All 12 tasks, where people get stuck and all 103 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
- Assess data quality and fitness for a stated use
- Review a proposed use, reuse, disclosure of or access to data and record the permitted-use decision
- 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
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- 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
- Diagnosing data quality exceptions
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Interpreting policy scope and delegated authority limits
All skills
- Designing and validating digital analytics event measurement
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Repairing data defects reversibly under owner authority
- Writing data quality rules
- Analysing staged funnels: conversion, time in stage and slippage
- 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
- 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
- Comparing periods consistently
- Constructing and challenging customer segments
- Controlling disclosure risk in shared data and outputs
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Designing and weighting rule-based scoring models
- Designing decision rights and dispute routes for data and AI work
- Designing efficient and reproducible investigations
- 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 digital funnel conversion from event data
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- 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
- Facilitating evidence-led retrospectives and corrective actions
- 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 keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting policy scope and delegated authority limits
- 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 and how to use AI for a task
- 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
- 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
- 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
- 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, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- 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 verifiable requirements and acceptance criteria
All tools
- HubSpot
- Salesforce
- Salesforce Marketing Cloud
- Salesforce Marketing Cloud Account Engagement (Pardot)
- Salesforce Data Loader
- Lime CRM
- Adobe Marketo Engage
- Microsoft Dynamics 365 Sales and Customer Insights
- Google Tag Manager
- Twilio Segment
- Microsoft Excel
- OneTrust
- Voyado
- Jira
- Zapier
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
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
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
About the role
Accountable for the marketing technology stack, data flows, reporting reliability and campaign to CRM handoffs. Decides system configuration, data rules and consent handling for marketing and lifecycle programmes without line management. Data judgement sits in keeping customer data accurate and consent-aware across tools.
Also called CRM Lead, Lifecycle Marketing Lead