Data Analyst
Accountable for answering commissioned business questions with reproducible SQL, spreadsheet and BI evidence.
3tasks in this job
26skills it uses
8tools named
The skills this job leans on most
- 1Establishing and declaring data grainNeeded in 2 of 3 tasks
- 2Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 2 of 3 tasks
- 3Reconciling conflicting reportsNeeded in 1 of 3 tasks
- 4Specifying BI measure semantics: additivity, aggregation and timeNeeded in 1 of 3 tasks
- 5Defining the analysis population: filters, nulls, dates and time zonesNeeded in 1 of 3 tasks
ToolsMicrosoft Power BISQL (query language)SAP S/4HANACollibraMicrosoft PurviewSnowflakeGoogle BigQueryMicrosoft Excel
All 3 tasks, where people get stuck and all 26 skills
Tasks
- Investigate inconsistent BI reporting
- Assess data quality and fitness for a stated use
- Answer a commissioned business question with reproducible query-based analysis
Where people get stuck
All skills
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Assessing data source credibility and fitness for a question
- Calculating and interpreting percentages, rates and ratios
- Choosing and justifying missing-data treatments
- Comparing groups
- Comparing periods consistently
- Decomposing one's own tasks for AI assistance
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- Designing efficient and reproducible investigations
- Diagnosing incorrect query and calculation results
- Eliciting the decision behind an analytical request
- Estimating quantities and differences with intervals
- Explaining how business events flow into accounting records
- Exploring data to find patterns that survive validation
- Investigating anomalies
- Judging whether a change exceeds normal variation
- Profiling data
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Selecting charts for evidence and decisions
- Standardising and cleaning data under agreed rules
- Validating records by matching, recomputation and end-to-end tracing
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
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
6tasks in this job
96skills it uses
12tools named
The skills this job leans on most
- 1Establishing and declaring data grainNeeded in 5 of 6 tasks
- 2Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 3 of 6 tasks
- 3Profiling dataNeeded in 3 of 6 tasks
- 4Writing data quality rulesNeeded in 3 of 6 tasks
- 5Diagnosing data quality exceptionsNeeded in 2 of 6 tasks
ToolsSQL (query language)Microsoft Power BISAP S/4HANACollibraMicrosoft PurviewSnowflake+ 6 more
All 6 tasks, where people get stuck and all 96 skills
Tasks
- Investigate inconsistent BI reporting
- Assess data quality and fitness for a stated use
- 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
- Evaluate statistical and causal evidence for an intervention's impact
Where people get stuck
All skills
- Combining tables safely: cardinality, fan-out and conservation of totals
- Diagnosing data quality exceptions
- Establishing and declaring data grain
- Profiling data
- Writing data quality rules
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying research ethics and participant consent
- 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
- 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
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing periods consistently
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- 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 experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- 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
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting correlation without inferring cause
- 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 a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- 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
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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 evidence-led findings and auditable recommendations with their limits
All tools
- SQL (query language)
- Microsoft Power BI
- SAP S/4HANA
- Collibra
- Microsoft Purview
- Snowflake
- Google BigQuery
- Microsoft Excel
- Python
- Tableau
- Qlik Sense
- Power Query
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
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
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
8tasks in this job
104skills it uses
12tools named
The skills this job leans on most
- 1Establishing and declaring data grainNeeded in 6 of 8 tasks
- 2Writing data quality rulesNeeded in 4 of 8 tasks
- 3Combining tables safely: cardinality, fan-out and conservation of totalsNeeded in 3 of 8 tasks
- 4Profiling dataNeeded in 3 of 8 tasks
- 5Diagnosing data quality exceptionsNeeded in 3 of 8 tasks
ToolsCollibraMicrosoft PurviewSQL (query language)SAP S/4HANASalesforceSnowflake+ 6 more
All 8 tasks, where people get stuck and all 104 skills
Tasks
- Define and govern a KPI metric contract
- Investigate inconsistent BI reporting
- Assess data quality and fitness for a stated use
- 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
- Evaluate statistical and causal evidence for an intervention's impact
- Set and maintain working standards for a team's analytical, reporting and modelling work products
Where people get stuck
All skills
- Combining tables safely: cardinality, fan-out and conservation of totals
- Diagnosing data quality exceptions
- Establishing and declaring data grain
- Profiling data
- Writing data quality rules
- Analysing event sequences, states and durations
- Applying data classification and handling rules
- Applying research ethics and participant consent
- 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
- 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
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Comparing periods consistently
- Constructing clear and honest charts
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing and interpreting hypothesis tests
- 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 experiments
- Designing implementable change plans
- Designing semantic models for governed analytics
- Diagnosing capability gaps from work evidence and planning development
- Diagnosing duplicate records
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating and improving chart accessibility
- Evaluating learning transfer and performance outcomes
- Evaluating whether people understood, adopted and sustained a change
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- 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 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 a change exceeds normal variation
- Judging whether uncertainty changes the decision
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Measuring campaign outcomes with valid denominators
- Monitoring data quality and freshness in production
- Preparing analysis-ready datasets reproducibly
- Presenting and defending evidence-based recommendations to decision-makers
- Reading and tracing unfamiliar code
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Reading data tables: rows, fields, types and units
- Recognising and resolving ethical conflicts in data and AI work
- Recognising misleading numerical claims
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconciling finance subledgers and close records
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- 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
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- 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 evidence-led findings and auditable recommendations with their limits
- Writing maintainable code for analytical and data work
All tools
- Collibra
- Microsoft Purview
- SQL (query language)
- SAP S/4HANA
- Salesforce
- Snowflake
- Google BigQuery
- Microsoft Excel
- Python
- Microsoft Power BI
- Tableau
- Power Query
Paths to start
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
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
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
About the role
Accountable for answering commissioned business questions with reproducible SQL, spreadsheet and BI evidence. Decides which sources and methods fit the question, judges whether the data is fit for use and states the limits of each finding. Delivers a finding for a specific decision, where a BI Developer delivers and maintains a reporting product.
Also called Business Intelligence Analyst, BI Analyst, MI Analyst, Reporting Analyst, Performance Analyst