People Operations Manager
Accountable for correct and controlled employment changes across joiner, mover and leaver processes and for the people operations team.
4tasks in this job
49skills it uses
7tools named
The skills this job leans on most
- 1Enforcing and auditing data accessNeeded in 2 of 4 tasks
- 2Applying data classification and handling rulesNeeded in 1 of 4 tasks
- 3Recalculating pay and incentive outcomes from plan rulesNeeded in 1 of 4 tasks
- 4Reconciling finance subledgers and close recordsCore to this role
- 5Verifying payment and bank-detail changes against fraud controlsCore to this role
ToolsVisma payroll (Visma Lønn, Visma Enterprise HRM, Visma Løn)WorkdaySympa HRiTrentHiBob (Bob)Microsoft Power BIMicrosoft Excel
All 4 tasks, where people get stuck and all 49 skills
Tasks
- Run a controlled payroll calculation and payment readiness check
- Govern employment joiner, mover and leaver changes
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Design data-dependent controls for a new or changed finance or people process, system or automation
Where people get stuck
- Diagnosing data quality exceptions
- Writing data quality rules
- Repairing data defects reversibly under owner authority
All skills
- Applying data classification and handling rules
- Enforcing and auditing data access
- Recalculating pay and incentive outcomes from plan rules
- Applying effective dating and retroactive change rules to records
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Classifying AI uses against regulatory obligations
- Designing and appraising samples
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Explaining control types, dependencies and compensating controls
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Managing master data identities and exceptions
- Mapping proposed process changes and control impacts
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Profiling data
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving and escalating pay, incentive and payment exceptions within authority
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- 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
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
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
7tasks in this job
74skills it uses
9tools named
The skills this job leans on most
- 1Diagnosing data quality exceptionsNeeded in 3 of 7 tasks
- 2Repairing data defects reversibly under owner authorityNeeded in 3 of 7 tasks
- 3Enforcing and auditing data accessNeeded in 3 of 7 tasks
- 4Writing data quality rulesNeeded in 1 of 7 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 1 of 7 tasks
ToolsMicrosoft ExcelSAP S/4HANAVisma payroll (Visma Lønn, Visma Enterprise HRM, Visma Løn)iTrentWorkdaySympa HR+ 3 more
All 7 tasks, where people get stuck and all 74 skills
Tasks
- Reconcile payroll to finance postings
- Run a controlled payroll calculation and payment readiness check
- Investigate and resolve an individual payroll exception
- Govern employment joiner, mover and leaver changes
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Design data-dependent controls for a new or changed finance or people process, system or automation
- Configure, test and release a change to a business system of record under change control
Where people get stuck
All skills
- Diagnosing data quality exceptions
- Enforcing and auditing data access
- Repairing data defects reversibly under owner authority
- Testing changes to data services and business systems for business meaning and downstream impact
- Writing data quality rules
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing change risk, collisions and go or no-go
- 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 the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing periods consistently
- Coordinating accountable handoffs between teams
- Designing and appraising samples
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing testable data controls
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- 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
- 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 policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping proposed process changes and control impacts
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recalculating pay and incentive outcomes from plan rules
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Resolving and escalating pay, incentive and payment exceptions within authority
- Running incident command: severity, timeline and status updates
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- 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 evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- Microsoft Excel
- SAP S/4HANA
- Visma payroll (Visma Lønn, Visma Enterprise HRM, Visma Løn)
- iTrent
- Workday
- Sympa HR
- Microsoft Power BI
- HiBob (Bob)
- ServiceNow
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
8tasks in this job
86skills it uses
10tools named
The skills this job leans on most
- 1Enforcing and auditing data accessNeeded in 5 of 8 tasks
- 2Diagnosing data quality exceptionsNeeded in 4 of 8 tasks
- 3Repairing data defects reversibly under owner authorityNeeded in 4 of 8 tasks
- 4Writing data quality rulesNeeded in 2 of 8 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 1 of 8 tasks
ToolsSAP S/4HANAVisma payroll (Visma Lønn, Visma Enterprise HRM, Visma Løn)iTrentWorkdaySympa HRServiceNow HR Service Delivery+ 4 more
All 8 tasks, where people get stuck and all 86 skills
Tasks
- Reconcile payroll to finance postings
- Run a controlled payroll calculation and payment readiness check
- Govern employment joiner, mover and leaver changes
- Assign data ownership and stewardship and decide a domain's data standards
- Test the design and operating effectiveness of a data-dependent control
- Monitor and correct data quality in a domain or system of record and drive remediation at source
- Design data-dependent controls for a new or changed finance or people process, system or automation
- Configure, test and release a change to a business system of record under change control
Where people get stuck
All skills
- Diagnosing data quality exceptions
- Enforcing and auditing data access
- Repairing data defects reversibly under owner authority
- Testing changes to data services and business systems for business meaning and downstream impact
- Writing data quality rules
- Applying data classification and handling rules
- Applying effective dating and retroactive change rules to records
- Assessing change risk, collisions and go or no-go
- 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 the control and reliability of end-user calculation files
- Assigning and operating data ownership and stewardship
- Building data protection by design into data products and pipelines
- Calculating and verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Comparing periods consistently
- Controlling disclosure risk in shared data and outputs
- Coordinating accountable handoffs between teams
- Designing and appraising samples
- Designing and validating digital analytics event measurement
- Designing and versioning data contracts for consumers
- 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 testable data controls
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- 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
- 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 policy scope and delegated authority limits
- Interpreting sustainability reporting standards and metric boundaries
- 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
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping entity results and eliminating intercompany balances in consolidation
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring and testing data control effectiveness
- Monitoring data quality and freshness in production
- Preparing change records with impact assessment and rollback
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading data tables: rows, fields, types and units
- Recalculating pay and incentive outcomes from plan rules
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Resolving and escalating pay, incentive and payment exceptions within 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
- Standardising and cleaning data under agreed rules
- Testing competing explanations with discriminating evidence
- Testing the completeness and accuracy of reports and data that controls rely on
- 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 evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- SAP S/4HANA
- Visma payroll (Visma Lønn, Visma Enterprise HRM, Visma Løn)
- iTrent
- Workday
- Sympa HR
- ServiceNow HR Service Delivery
- HiBob (Bob)
- Microsoft Power BI
- Microsoft Excel
- ServiceNow
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 correct and controlled employment changes across joiner, mover and leaver processes and for the people operations team. Decides process rules, exception outcomes and priorities for HR systems and payroll inputs. Data judgement sits in the quality of HR records and the controls that protect pay and personal data.
Also called HR Operations Manager, Payroll Manager, HR Manager (when the role manages an HR team)