Reward Manager
Accountable for pay structures, benchmarking, job evaluation and the annual pay review and for the reward team that runs them.
4tasks in this job
53skills it uses
5tools named
The skills this job leans on most
- 1Analysing pay equity with raw and adjusted pay gapsNeeded in 3 of 4 tasks
- 2Evaluating jobs with an analytical, gender-neutral schemeNeeded in 3 of 4 tasks
- 3Benchmarking jobs to market pay data and designing pay rangesNeeded in 2 of 4 tasks
- 4Costing a pay review budget and guideline matrixNeeded in 2 of 4 tasks
ToolsExcel VBA and macrosMicrosoft ExcelWorkdayMicrosoft Power BIMercer compensation data and job evaluationWTW compensation surveysSAP S/4HANASAP SuccessFactorsMicrosoft PowerPoint
All 4 tasks, where people get stuck and all 53 skills
Tasks
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- Benchmark jobs and set pay ranges using objective job evaluation
- Model and recommend the annual pay and benefits review budget and guidelines
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Controlling disclosure risk in shared data and outputs
- Answering individual pay transparency requests
All skills
- Analysing pay equity with raw and adjusted pay gaps
- Costing a pay review budget and guideline matrix
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing staged funnels: conversion, time in stage and slippage
- Answering individual pay transparency requests
- Applying data classification and handling rules
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building driver-based financial forecasts
- Communicating quantified uncertainty
- Comparing groups
- Constructing a controlled cash-flow plan
- Controlling disclosure risk in shared data and outputs
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing implementable change plans
- Designing testable data controls
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Evaluating and improving chart accessibility
- Explaining how business events flow into accounting records
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Giving specific developmental feedback on data and AI work
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Testing competing explanations with discriminating evidence
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
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
Coming soonPeople and Workforce Analytics
5tasks in this job
72skills it uses
9tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 4 of 5 tasks
- 2Analysing pay equity with raw and adjusted pay gapsNeeded in 3 of 5 tasks
- 3Evaluating jobs with an analytical, gender-neutral schemeNeeded in 3 of 5 tasks
- 4Benchmarking jobs to market pay data and designing pay rangesNeeded in 2 of 5 tasks
- 5Answering individual pay transparency requestsNeeded in 1 of 5 tasks
ToolsExcel VBA and macrosMicrosoft ExcelWorkdayMicrosoft Power BIMercer compensation data and job evaluationWTW compensation surveys+ 3 more
All 5 tasks, where people get stuck and all 72 skills
Tasks
- Automate a recurring reporting or data task with controls and a named owner
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- Benchmark jobs and set pay ranges using objective job evaluation
- Model and recommend the annual pay and benefits review budget and guidelines
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Benchmarking jobs to market pay data and designing pay ranges
- Costing a pay review budget and guideline matrix
All skills
- Analysing pay equity with raw and adjusted pay gaps
- Answering individual pay transparency requests
- Benchmarking jobs to market pay data and designing pay ranges
- Controlling disclosure risk in shared data and outputs
- Evaluating jobs with an analytical, gender-neutral scheme
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing staged funnels: conversion, time in stage and slippage
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Assessing data-driven fairness and bias
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- 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
- Communicating quantified uncertainty
- Comparing groups
- Constructing a controlled cash-flow plan
- Costing a pay review budget and guideline matrix
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating and improving chart accessibility
- Explaining how business events flow into accounting records
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- 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 keys and testing referential integrity
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- 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
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Variance analysis
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
Paths to start
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- 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
Coming soonPeople and Workforce Analytics
7tasks in this job
100skills it uses
10tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 5 of 7 tasks
- 2Analysing pay equity with raw and adjusted pay gapsNeeded in 3 of 7 tasks
- 3Evaluating jobs with an analytical, gender-neutral schemeNeeded in 3 of 7 tasks
- 4Benchmarking jobs to market pay data and designing pay rangesNeeded in 2 of 7 tasks
- 5Answering individual pay transparency requestsNeeded in 1 of 7 tasks
ToolsExcel VBA and macrosMicrosoft ExcelMicrosoft Power BIMicrosoft SharePointWorkdaySAP SuccessFactors+ 4 more
All 7 tasks, where people get stuck and all 100 skills
Tasks
- Automate a recurring reporting or data task with controls and a named owner
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- 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
- Benchmark jobs and set pay ranges using objective job evaluation
- Model and recommend the annual pay and benefits review budget and guidelines
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Benchmarking jobs to market pay data and designing pay ranges
- Costing a pay review budget and guideline matrix
All skills
- Analysing pay equity with raw and adjusted pay gaps
- Answering individual pay transparency requests
- Benchmarking jobs to market pay data and designing pay ranges
- Controlling disclosure risk in shared data and outputs
- Evaluating jobs with an analytical, gender-neutral scheme
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing staged funnels: conversion, time in stage and slippage
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- 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 decision-focused reports and dashboards
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing groups
- Constructing a controlled cash-flow plan
- Costing a pay review budget and guideline matrix
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing enforceable data governance policies
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- 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
- Evaluating and improving chart accessibility
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- 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 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 missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating procurement spend leakage and duplicate commitments
- Investigating workforce movement and attrition trends
- 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
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Preserving incident evidence and timeline
- Profiling data
- Reading and tracing unfamiliar code
- Reading data tables: rows, fields, types and units
- Recognising and containing suspected personal data breaches
- Recognising cybersecurity threats to data work
- Recognising personal data and privacy duties
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- 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
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Variance analysis
- 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 maintainable code for analytical and data work
- Writing testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Excel VBA and macros
- Microsoft Excel
- Microsoft Power BI
- Microsoft SharePoint
- Workday
- SAP SuccessFactors
- Mercer compensation data and job evaluation
- WTW compensation surveys
- SAP S/4HANA
- Microsoft PowerPoint
Paths to start
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
Privacy, Security and Access for Data and AI
Protect personal data and access with impact assessments and incident evidence.
- 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
Coming soonPeople and Workforce Analytics
About the role
Accountable for pay structures, benchmarking, job evaluation and the annual pay review and for the reward team that runs them. Decides pay ranges, review budgets and benefit design within delegated authority and owns pay gap and pay transparency reporting. Data judgement sits in modelling pay fairly and controlling disclosure of pay data, where a People Operations Manager runs payroll inputs and employment changes.
Also called Compensation and Benefits Manager, Pay and Reward Manager