Analytics Engineer
Accountable for tested and documented transformations and semantic models that implement agreed metric definitions in the warehouse or lakehouse.
3tasks in this job
18skills it uses
6tools named
The skills this job leans on most
- 1Designing dimensional models with explicit grainNeeded in 3 of 3 tasks
- 2Designing relational schemas: keys, constraints and normalisationNeeded in 2 of 3 tasks
- 3Engineering production data transformationsNeeded in 2 of 3 tasks
ToolsdbtApache AirflowSQL (query language)PythonGit / GitHubGitLabSnowflakeGoogle BigQueryDatabricksJiraMicrosoft Power BI
All 3 tasks, where people get stuck and all 18 skills
Tasks
- Define and govern a KPI metric contract
- Diagnose a data pipeline failure and recover safely
- Design and build a tested, governed analytical data model
Where people get stuck
- Tracing data lineage upstream and downstream
- Designing and versioning data contracts for consumers
- Testing changes to data services and business systems for business meaning and downstream impact
All skills
- Designing dimensional models with explicit grain
- Assigning and operating data ownership and stewardship
- Building decision-focused reports and dashboards
- Designing automated pipelines and processes that fail safely and recover
- Designing semantic models for governed analytics
- Gating releases on evidence with rollback ready
- Identifying keys and testing referential integrity
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Recovering data pipelines safely
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Tracing data lineage upstream and downstream
- Writing data quality rules
Paths to start
Coming soonAnalytics Engineering with dbtAnalytics Engineering: Tested, Version-controlled Transformation
6tasks in this job
65skills it uses
11tools named
The skills this job leans on most
- 1Tracing data lineage upstream and downstreamNeeded in 5 of 6 tasks
- 2Designing relational schemas: keys, constraints and normalisationNeeded in 4 of 6 tasks
- 3Designing dimensional models with explicit grainNeeded in 4 of 6 tasks
- 4Designing and versioning data contracts for consumersNeeded in 4 of 6 tasks
- 5Testing changes to data services and business systems for business meaning and downstream impactNeeded in 4 of 6 tasks
ToolsdbtApache AirflowSQL (query language)PythonGit / GitHubGitLab+ 5 more
All 6 tasks, where people get stuck and all 65 skills
Tasks
- Define and govern a KPI metric contract
- Diagnose a data pipeline failure and recover safely
- Assess data quality and fitness for a stated use
- Prepare and verify a data-service release or migration for change approval
- Design and build a tested, governed analytical data model
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
- Testing changes to data services and business systems for business meaning and downstream impact
- Engineering production data transformations
All skills
- Designing and versioning data contracts for consumers
- Designing dimensional models with explicit grain
- Designing relational schemas: keys, constraints and normalisation
- Testing changes to data services and business systems for business meaning and downstream impact
- Tracing data lineage upstream and downstream
- Applying data classification and handling rules
- 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
- 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 verifying KPIs against their specification
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- Designing and validating batch and streaming ingestion
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing operational handoffs and flow
- Enforcing and auditing data access
- Engineering production data transformations
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Explaining technical work to accountable non-specialists
- 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 policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Managing master data identities and exceptions
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing change records with impact assessment and rollback
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- Reading business metrics
- Recognising and containing suspected personal data breaches
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Recovering data pipelines safely
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Running incident command: severity, timeline and status updates
- 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 competing explanations with discriminating evidence
- Understanding and interrogating data and application APIs
- Validating records by matching, recomputation and end-to-end tracing
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing maintainable code for analytical and data work
- 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
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
Coming soonAnalytics Engineering: Tested, Version-controlled TransformationData Platform Architecture, Integration and Economics
6tasks in this job
74skills it uses
6tools named
The skills this job leans on most
- 1Tracing data lineage upstream and downstreamNeeded in 6 of 6 tasks
- 2Designing relational schemas: keys, constraints and normalisationNeeded in 5 of 6 tasks
- 3Testing changes to data services and business systems for business meaning and downstream impactNeeded in 5 of 6 tasks
- 4Designing dimensional models with explicit grainNeeded in 4 of 6 tasks
- 5Designing and versioning data contracts for consumersNeeded in 3 of 6 tasks
ToolsSQL (query language)LookerApache AirflowGit / GitHubGitLabdbt
All 6 tasks, where people get stuck and all 74 skills
Tasks
- Define and govern a KPI metric contract
- Diagnose a data pipeline failure and recover safely
- Prepare and verify a data-service release or migration for change approval
- Design and build a tested, governed analytical data model
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
- Set engineering standards for testing, change, data handling and AI-assisted code
Where people get stuck
- Testing changes to data services and business systems for business meaning and downstream impact
- Engineering production data transformations
All skills
- Designing and versioning data contracts for consumers
- Designing dimensional models with explicit grain
- Designing relational schemas: keys, constraints and normalisation
- Testing changes to data services and business systems for business meaning and downstream impact
- Tracing data lineage upstream and downstream
- Applying data classification and handling rules
- 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
- 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 verifying KPIs against their specification
- Coordinating accountable handoffs between teams
- Debugging data and application services from evidence
- Designing and validating batch and streaming ingestion
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Enforcing and auditing data access
- Engineering production data transformations
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- 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
- 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
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Planning and validating controlled data migrations
- Preparing change records with impact assessment and rollback
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading and tracing unfamiliar code
- Reading business metrics
- Recognising and containing suspected personal data breaches
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Recovering data pipelines safely
- Repairing data defects reversibly under owner authority
- Reviewing human- and AI-generated code changes for correctness and risk
- Running incident command: severity, timeline and status updates
- Sampling and reviewing a team's work against standards
- 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 competing explanations with discriminating evidence
- Understanding and interrogating data and application APIs
- 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
- Writing maintainable code for analytical and data work
- 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
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
Coming soonAnalytics Engineering: Tested, Version-controlled TransformationData Platform Architecture, Integration and Economics
About the role
Accountable for tested and documented transformations and semantic models that implement agreed metric definitions in the warehouse or lakehouse. Decides how data is modelled for analysis and which tests protect it. Sits between the Data Engineer who lands data and the BI Developer who builds reports on the modelled layer.