People Analyst
Accountable for sound evidence on workforce movement, attrition and pay equity with controlled disclosure of sensitive data.
4tasks in this job
51skills it uses
5tools named
The skills this job leans on most
- 1Verifying reports and dashboards before and after releaseNeeded in 4 of 4 tasks
- 2Building decision-focused reports and dashboardsNeeded in 1 of 4 tasks
- 3Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 4 tasks
ToolsWorkdayMicrosoft Power BIPythonR (with RStudio / Posit)QualtricsMicrosoft SQL Server (with SSMS)Power QueryMicrosoft ExcelTableauSympa HR
All 4 tasks, where people get stuck and all 51 skills
Tasks
- Assess workforce movement and attrition
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Analyse absence patterns and recommend interventions
Where people get stuck
- Designing and appraising samples
- Controlling disclosure risk in shared data and outputs
- Interpreting workforce metrics and populations
All skills
- Building decision-focused reports and dashboards
- Specifying BI measure semantics: additivity, aggregation and time
- Analysing event sequences, states and durations
- 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 interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Comparing groups
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing incorrect query and calculation results
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Estimating quantities and differences with intervals
- Exploring data to find patterns that survive validation
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting workforce metrics and populations
- Investigating anomalies
- Investigating workforce movement and attrition trends
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Profiling data
- 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 personal data and privacy duties
- Reconciling conflicting reports
- Selecting charts for evidence and decisions
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Tracing data lineage upstream and downstream
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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 Visualisation and Dashboard Design
Design honest, accessible charts and decision-useful dashboards and verify them before release.
- Modules
- 5 open
- Practice data
- Raket Parts
8tasks in this job
82skills it uses
10tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 6 of 8 tasks
- 2Interpreting workforce metrics and populationsNeeded in 5 of 8 tasks
- 3Investigating workforce movement and attrition trendsNeeded in 5 of 8 tasks
- 4Designing and appraising samplesCore to this role
- 5Evaluating employee experience evidence responsiblyCore to this role
ToolsWorkdayMicrosoft Power BIPythonR (with RStudio / Posit)QualtricsMicrosoft SQL Server (with SSMS)+ 4 more
All 8 tasks, where people get stuck and all 82 skills
Tasks
- Assess workforce movement and attrition
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- Analyse compensation equity with controlled disclosure and answer individual pay transparency requests
- Model the workforce plan and organisation design options with cost and people impact
- Analyse absence patterns and recommend interventions
- Assess succession cover and talent pipeline risk for critical roles
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
All skills
- Controlling disclosure risk in shared data and outputs
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Analysing pay equity with raw and adjusted pay gaps
- Analysing staged funnels: conversion, time in stage and slippage
- Answering individual pay transparency requests
- 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
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing groups
- Comparing options against explicit decision criteria
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- 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
- Exploring data to find patterns that survive validation
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- Gating releases on evidence with rollback ready
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Locating and acting on personal data for individual rights requests
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Modelling workforce demand and supply
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconciling conflicting reports
- Resolving consent and suppression status across systems
- Running sensitivity and break-even analysis on model assumptions
- Selecting charts for evidence and decisions
- 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
- Testing changes to data services and business systems for business meaning and downstream impact
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
Statistics for Evidence-led Decisions
Use and communicate statistical evidence for decisions.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonPeople and Workforce Analytics
10tasks in this job
98skills it uses
11tools named
The skills this job leans on most
- 1Controlling disclosure risk in shared data and outputsNeeded in 6 of 10 tasks
- 2Interpreting workforce metrics and populationsNeeded in 5 of 10 tasks
- 3Analysing pay equity with raw and adjusted pay gapsNeeded in 5 of 10 tasks
- 4Investigating workforce movement and attrition trendsNeeded in 4 of 10 tasks
- 5Applying effective dating and retroactive change rules to recordsCore to this role
ToolsR (with RStudio / Posit)PythonMicrosoft Power BIMicrosoft SQL Server (with SSMS)Power QueryTableau+ 5 more
All 10 tasks, where people get stuck and all 98 skills
Tasks
- Assess workforce movement and attrition
- Define and govern a KPI metric contract
- Answer a commissioned business question with reproducible query-based analysis
- Deliver and sustain a trusted BI report or dashboard
- 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
- Analyse absence patterns and recommend interventions
- Assess succession cover and talent pipeline risk for critical roles
- Set disclosure control rules for sensitive data outputs and review exceptions
Where people get stuck
- Applying effective dating and retroactive change rules to records
- Answering individual pay transparency requests
All skills
- Analysing pay equity with raw and adjusted pay gaps
- Controlling disclosure risk in shared data and outputs
- Interpreting workforce metrics and populations
- Investigating workforce movement and attrition trends
- Analysing event sequences, states and durations
- Analysing organisation structure, spans and critical-role cover
- Analysing staged funnels: conversion, time in stage and slippage
- Answering individual pay transparency requests
- 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
- Calculating and interpreting percentages, rates and ratios
- Calculating and verifying KPIs against their specification
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing groups
- Comparing options against explicit decision criteria
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and versioning data contracts for consumers
- Designing auditable decision models
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing implementable change plans
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing incorrect query and calculation results
- 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 quantities and differences with intervals
- Evaluating and improving chart accessibility
- Exploring data to find patterns that survive validation
- Finding relevant data sources
- Fitting, checking and interpreting inferential regression models
- Formulating answerable data questions
- 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 correlation without inferring cause
- Interpreting missing and unavailable data
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- 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
- Mapping stakeholders' decisions, constraints and needs
- Modelling workforce demand and supply
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Profiling data
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Reconciling conflicting reports
- Resolving consent and suppression status across systems
- Reviewing human- and AI-generated code changes for correctness and risk
- Running sensitivity and break-even analysis on model assumptions
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- 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
- 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
- Training reproducible machine learning models
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- 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
- R (with RStudio / Posit)
- Python
- Microsoft Power BI
- Microsoft SQL Server (with SSMS)
- Power Query
- Tableau
- Microsoft Excel
- Mercer compensation data and job evaluation
- Workday
- Sympa HR
- Qualtrics
Paths to start
Skill Path
Exploratory Data Analysis and Pattern Discovery
Describe a distribution honestly and decide which unusual records need checking before anyone acts.
- 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
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
Coming soonPeople and Workforce Analytics
About the role
Accountable for sound evidence on workforce movement, attrition and pay equity with controlled disclosure of sensitive data. Decides analysis methods, groupings and suppression within agreed standards. The HR Business Partner shapes the people decision while the People Analyst produces and explains the evidence behind it.
Also called HR Analyst, Reward Analyst