Lead Analytics Engineer
Accountable for the modelling and testing standards of the analytics layer and for reviewing other analytics engineers' changes.
3tasks in this job
34skills it uses
6tools named
The skills this job leans on most
- 1Designing relational schemas: keys, constraints and normalisationNeeded in 2 of 3 tasks
- 2Designing dimensional models with explicit grainNeeded in 2 of 3 tasks
- 3Engineering production data transformationsNeeded in 1 of 3 tasks
ToolsdbtLookerSQL (query language)SnowflakeGoogle BigQueryDatabricksMicrosoft PurviewJiraMicrosoft Power BIGit / GitHubGitLabApache Airflow
All 3 tasks, where people get stuck and all 34 skills
Tasks
- Define and govern a KPI metric contract
- Design and build a tested, governed analytical data model
- Set engineering standards for testing, change, data handling and AI-assisted code
Where people get stuck
- Reviewing human- and AI-generated code changes for correctness and risk
- Designing testable data controls
- Writing testable work standards and review checklists
All skills
- Applying data classification and handling rules
- 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
- Designing and versioning data contracts for consumers
- Designing automated pipelines and processes that fail safely and recover
- Designing semantic models for governed analytics
- Diagnosing misleading data visualisations
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- 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
- Judging the materiality of defects and deficiencies for the intended use
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Reading and tracing unfamiliar code
- Reading business metrics
- Reconciling conflicting reports
- Reviewing human- and AI-generated code changes for correctness and risk
- 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
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Writing data quality rules
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
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonAnalytics Engineering: Tested, Version-controlled Transformation
4tasks in this job
68skills it uses
12tools named
The skills this job leans on most
- 1Reviewing human- and AI-generated code changes for correctness and riskNeeded in 4 of 4 tasks
- 2Designing relational schemas: keys, constraints and normalisationNeeded in 3 of 4 tasks
- 3Designing dimensional models with explicit grainNeeded in 3 of 4 tasks
- 4Engineering production data transformationsNeeded in 2 of 4 tasks
- 5Designing testable data controlsCore to this role
ToolsdbtLookerSQL (query language)SnowflakeGoogle BigQueryDatabricks+ 6 more
All 4 tasks, where people get stuck and all 68 skills
Tasks
- Define and govern a KPI metric contract
- 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
All skills
- Designing relational schemas: keys, constraints and normalisation
- Engineering production data transformations
- Reviewing human- and AI-generated code changes for correctness and risk
- Applying data classification and handling rules
- 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 verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Controlling disclosure risk in shared data and outputs
- 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 implementable change plans
- 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
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating and improving chart accessibility
- 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
- 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
- Managing master data identities and exceptions
- 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
- Reading and tracing unfamiliar code
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- 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 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
- 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 testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonAnalytics Engineering: Tested, Version-controlled Transformation
5tasks in this job
69skills it uses
9tools named
The skills this job leans on most
- 1Reviewing human- and AI-generated code changes for correctness and riskNeeded in 5 of 5 tasks
- 2Designing relational schemas: keys, constraints and normalisationNeeded in 4 of 5 tasks
- 3Designing dimensional models with explicit grainNeeded in 4 of 5 tasks
- 4Engineering production data transformationsNeeded in 2 of 5 tasks
- 5Designing testable data controlsCore to this role
ToolsLookerMicrosoft Power BIdbtGit / GitHubGitLabMicrosoft Purview+ 3 more
All 5 tasks, where people get stuck and all 69 skills
Tasks
- Define and govern a KPI metric contract
- 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
- Define the target data architecture, principles and roadmap
- Set engineering standards for testing, change, data handling and AI-assisted code
Where people get stuck
All skills
- Designing relational schemas: keys, constraints and normalisation
- Engineering production data transformations
- Reviewing human- and AI-generated code changes for correctness and risk
- Applying data classification and handling rules
- 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 verifying KPIs against their specification
- Classifying AI uses against regulatory obligations
- Controlling disclosure risk in shared data and outputs
- 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 implementable change plans
- 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
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Evaluating and improving chart accessibility
- 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
- 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
- Managing master data identities and exceptions
- 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
- Reading and tracing unfamiliar code
- Reading business metrics
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- 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 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
- 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 testable work standards and review checklists
- Writing verifiable requirements and acceptance criteria
All tools
- Looker
- Microsoft Power BI
- dbt
- Git / GitHub
- GitLab
- Microsoft Purview
- SQL (query language)
- Snowflake
- Databricks
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
Business Data Controls and Reconciliation
Design, test and operate controls and reconciliations.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Leading Analytics and AI Change
Lead adoption, diagnose learning needs, coach and assure team capability.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonAnalytics Engineering: Tested, Version-controlled Transformation
About the role
Accountable for the modelling and testing standards of the analytics layer and for reviewing other analytics engineers' changes. Sets conventions for a whole warehouse or lakehouse layer and decides how shared marts are structured. Leads this work without line management, where an Analytics Engineer builds one subject area.