Analytics Engineering
Analytics Engineering is an individual-contributor Data + AI career progressing from guided delivery to enterprise expertise without requiring people management.
- Function
- Data & Analytics
- Archetype
- Engineering
- Highest level
- L6 · Principal Analytics Engineer
Why it exists
Transforms modeled data into governed analytical datasets, semantic models and trusted metrics for self-service analytics.
Typical responsibilities
Deliver analytics engineering outcomes; apply data/AI standards; manage quality, reliability and risk; collaborate across product, engineering, security, legal and business teams; at senior levels shape enterprise standards and strategy.
Where the work happens
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
How the career progresses
Associate Analytics Engineer → Analytics Engineer → Senior Analytics Engineer → Staff Analytics Engineer → Senior Staff Analytics Engineer → Principal Analytics Engineer
Levels in this career
Six standard stages. The seventh exists only where the career provides for it.
What is expected at L2 · Analytics Engineer
Apply analytics engineering capability at independently delivers standard work within a team or defined domain.
The five dimensions that change
- Autonomy
- Independent routine ownership
- Scope
- Independently delivers standard work within a team or defined domain.
- Complexity
- Moderate
- Influence
- Immediate team
- Business impact
- Task/team
- Ambiguity
- Moderate
What good looks like
Produces trustworthy analytics engineering outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L2.
Typical evidence
Completed analytics engineering work products or production outcomes; documented decisions; measurable improvements in quality, reliability, risk, speed, cost or business outcomes; peer/stakeholder evidence of L2 scope.
Skills expected at L2
Grouped as human, professional and technical. Target proficiency uses the P1–P7 scale, and each row says what that level means for that particular skill.
Human8
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalSupporting | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalSupporting | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalSupporting | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalSupporting | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalSupporting | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalSupporting | Applies Structured problem solving to routine situations using established methods and controls. |
Professional7
| Professional | P | Supporting |
|---|---|---|
| Analytics problem framingData & Analytics Practice | P2FoundationalCore | Applies Analytics problem framing to routine situations using established methods and controls. |
| Data contracts governanceData & Analytics Practice | P2FoundationalSupporting | Applies Data contracts governance to routine situations using established methods and controls. |
| Data governanceCybersecurity, Privacy & Data Governance Standards | P2FoundationalSupporting | Applies Data governance to routine situations using established methods and controls. |
| Data product managementData & Analytics Practice | P2FoundationalSupporting | Applies Data product management to routine situations using established methods and controls. |
| Data quality managementCybersecurity, Privacy & Data Governance Standards | P2FoundationalSupporting | Applies Data quality management to routine situations using established methods and controls. |
| KPI designData & Analytics Practice | P2FoundationalCore | Applies KPI design to routine situations using established methods and controls. |
| Metrics definitionData & Analytics Practice | P2FoundationalCore | Applies Metrics definition to routine situations using established methods and controls. |
Technical10
| Technical | P | Supporting |
|---|---|---|
| Dashboard developmentAnalytics, BI & Data Visualization | P2FoundationalSupporting | Uses Dashboard development for routine tasks with documented patterns and review. |
| Data catalog platformsDatabases & Data Platforms | P2FoundationalSupporting | Uses Data catalog platforms for routine tasks with documented patterns and review. |
| Data contract implementationDatabases & Data Platforms | P2FoundationalSupporting | Uses Data contract implementation for routine tasks with documented patterns and review. |
| Data lineage toolingDatabases & Data Platforms | P2FoundationalSupporting | Uses Data lineage tooling for routine tasks with documented patterns and review. |
| Data modelingDatabases & Data Platforms | P2FoundationalCore | Uses Data modeling for routine tasks with documented patterns and review. |
| Data visualization implementationAnalytics, BI & Data Visualization | P2FoundationalSupporting | Uses Data visualization implementation for routine tasks with documented patterns and review. |
| Dimensional modelingDatabases & Data Platforms | P2FoundationalCore | Uses Dimensional modeling for routine tasks with documented patterns and review. |
| SQL analyticsAnalytics, BI & Data Visualization | P2FoundationalCore | Uses SQL analytics for routine tasks with documented patterns and review. |
| Semantic layer engineeringAnalytics, BI & Data Visualization | P2FoundationalCore | Uses Semantic layer engineering for routine tasks with documented patterns and review. |
| dbtDatabases & Data Platforms | P2FoundationalCore | Uses dbt for routine tasks with documented patterns and review. |
What changes from L2 to L3
Moving from Analytics Engineer to Senior Analytics Engineer means greater autonomy, complexity, scope, influence and evidence of impact—not simply more tools.
How readiness is shown
Sustained evidence of operating at L3-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
How to prepare
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Adjacent careers
Computed from shared skills. It is a signal for exploring, not a hiring or eligibility guarantee.
- Data ArchitectureData & Analytics21 shared skills
- AI Governance & Responsible AIData & Analytics12 shared skills
- AI Platform & MLOps EngineeringData & Analytics12 shared skills
- Data AnalyticsData & Analytics15 shared skills
- Business IntelligenceData & Analytics15 shared skills
- Data EngineeringData & Analytics15 shared skills
Career framework v21, active since August 17, 2026.