Data Architecture
Data Architecture is an individual-contributor Data + AI career progressing from guided delivery to enterprise expertise without requiring people management.
- Function
- Data & Analytics
- Archetype
- Architecture
- Highest level
- L7 · Distinguished Data Architect
Why it exists
Defines enterprise data architecture, models, integration patterns, platform boundaries and governance principles for scalable trusted data products.
Typical responsibilities
Deliver data architecture 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 Data Architect → Data Architect → Senior Data Architect → Staff Data Architect → Principal Data Architect → Senior Principal Data Architect → Distinguished Data Architect
Levels in this career
Six standard stages. The seventh exists only where the career provides for it.
- L1Associate Data Architect
- L2Data Architect
- L3Senior Data Architect
- L4Staff Data Architect
- L5Principal Data Architect
- L6Senior Principal Data Architect
- L7Distinguished Data Architect
Optional distinguished level. Not every career reaches it.
What is expected at L2 · Data Architect
Apply data architecture 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 data architecture outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L2.
Typical evidence
Completed data architecture 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 |
|---|---|---|
| Architecture governanceIT Service Management & Governance | P2FoundationalSupporting | Applies Architecture governance to routine situations using established methods and controls. |
| Data governanceCybersecurity, Privacy & Data Governance Standards | P2FoundationalCore | Applies Data governance to routine situations using established methods and controls. |
| Data mesh operating modelData & Analytics Practice | P2FoundationalSupporting | Applies Data mesh operating model 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 | P2FoundationalCore | Applies Data quality management to routine situations using established methods and controls. |
| Data stewardshipCybersecurity, Privacy & Data Governance Standards | P2FoundationalCore | Applies Data stewardship to routine situations using established methods and controls. |
| Technology governanceIT Service Management & Governance | P2FoundationalSupporting | Applies Technology governance to routine situations using established methods and controls. |
Technical10
| Technical | P | Supporting |
|---|---|---|
| Apache KafkaDatabases & Data Platforms | P2FoundationalSupporting | Uses Apache Kafka 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 lake / lakehouse engineeringDatabases & Data Platforms | P2FoundationalCore | Uses Data lake / lakehouse engineering 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 mesh architectureDatabases & Data Platforms | P2FoundationalCore | Uses Data mesh architecture 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 warehouse engineeringDatabases & Data Platforms | P2FoundationalCore | Uses Data warehouse engineering for routine tasks with documented patterns and review. |
| ER modelingArchitecture & Modeling Tools | P2FoundationalCore | Uses ER modeling for routine tasks with documented patterns and review. |
| Semantic layer engineeringAnalytics, BI & Data Visualization | P2FoundationalSupporting | Uses Semantic layer engineering for routine tasks with documented patterns and review. |
What changes from L2 to L3
Moving from Data Architect to Senior Data Architect 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.
- Analytics EngineeringData & Analytics21 shared skills
- AI Platform & MLOps EngineeringData & Analytics14 shared skills
- Data EngineeringData & Analytics15 shared skills
- AI Governance & Responsible AIData & Analytics12 shared skills
- Data AnalyticsData & Analytics15 shared skills
- Business IntelligenceData & Analytics15 shared skills
Career framework v21, active since August 17, 2026.