| name | data-architect |
| description | Design data architecture at enterprise and solution levels.
Cover data mesh, lakehouse, governance, domain-driven design, conceptual/logical/physical data modeling,
platform selection, and compliance frameworks. Produce ADRs, data model diagrams, platform comparison
matrices, and governance policy templates.
Triggers on "design data platform", "choose data warehouse", "data mesh", "lakehouse architecture",
"data governance", "data modeling", "platform selection", "data architecture decision",
"compliance framework", or "data strategy". For applied AI solution architecture (RAG data plane,
embeddings, vector stores in commercial or enterprise products), use
applied-ai-architect-commercial-enterprise. For dbt analytics layers and mart delivery, use
analytics-data-engineer—not data-architect.
|
Data Architect
Overview
Design data architecture at enterprise and solution levels. This skill covers data mesh, lakehouse,
governance, domain-driven design, conceptual/logical/physical data modeling, platform selection,
and compliance frameworks. Produce ADRs, data model diagrams, platform comparison matrices,
and governance policy templates.
When to Use
- Choosing among warehouse, lake, lakehouse, mesh, or streaming-first patterns
- Creating conceptual, logical, or physical data models and ADRs
- Defining data governance, catalog, quality, and compliance frameworks
- Evaluating data platforms and long-term TCO or vendor trade-offs
When NOT to Use
- Day-to-day pipeline on-call, SLA breaches, or shift handoffs → use
data-system-ops-lead
- Single-platform SQL tuning or star-schema implementation detail → use
data-warehouse-engineer
- dbt project implementation, mart tests, and analytics CI → use
analytics-data-engineer
- Team roadmaps, sprint cadence, or governance operations execution → use
data-manager
- OWL/RDF ontologies or knowledge-graph construction → use
ontology-engineer
- Application integration patterns and non-data system ADRs → use
senior-system-architecture
- LLM/RAG/copilot solution architecture and AI ADRs → use
applied-ai-architect-commercial-enterprise
Features
- Architecture decision framework with weighted criteria evaluation
- Progressive data modeling workflow (conceptual → logical → physical)
- Platform selection decision tree for warehouse/lake/lakehouse/mesh/streaming
- Governance pillar planning with tool recommendations
- ADR template generation and stakeholder review processes
Usage
- Identify the user's data architecture need (platform choice, modeling, governance, or decision framework)
- Follow the corresponding workflow below
- Produce structured outputs: ADRs, data model diagrams, platform comparison matrices, or governance policies
Examples
-
User: "Should we use a data lake or data warehouse for our analytics?"
Agent: Runs Platform & Technology Selection workflow (Workflow 3), evaluates structured vs raw data needs, recommends warehouse/lake/lakehouse with trade-offs
-
User: "We need to model our customer domain"
Agent: Runs Data Modeling Workflow (Workflow 2), starts with conceptual model (entities, relationships), progresses to logical ER diagram, then physical DDL
-
User: "How do we set up data governance for GDPR compliance?"
Agent: Runs Governance & Compliance Planning (Workflow 4), maps GDPR requirements to governance pillars, recommends tools and controls
Core Workflows
1. Architecture Decision Framework
Use this 5-step process for any major data architecture decision:
-
Define the decision context
- Business drivers (scale, latency, cost, compliance)
- Constraints (budget, timeline, existing tech, team skills)
- Stakeholders (data engineers, analysts, product, legal)
-
Identify alternatives
- At least 3 options (do nothing, minimal change, transformative)
- Include cloud-native, hybrid, and open-source alternatives
-
Evaluate against criteria
| Criterion | Weight | Score 1-5 each option |
|---|
| Scalability | High | |
| Cost (TCO 3yr) | High | |
| Time to value | Medium | |
| Operational complexity | Medium | |
| Team fit | Medium | |
| Vendor lock-in risk | Low | |
-
Assess risks & mitigation
- Migration risk, talent risk, operational risk
- POC plan for the top 2 options
-
Document the decision
- ADR (Architecture Decision Record) with context, decision, consequences
- Share with stakeholders; revisit quarterly
2. Data Modeling Workflow
Progressive refinement from business to implementation:
| Stage | Output | Audience | Key Activities |
|---|
| Conceptual | Entity list, relationships, business glossary | Business stakeholders | Workshops, domain events |
| Logical | Normalized ER diagram, attributes, keys | Data analysts, architects | Identify entities, resolve many-to-many |
| Physical | DB-specific DDL, partitions, indexes | Engineers | Platform optimization, denormalization |
Key principles:
- Start with the business question, not the technology
- Use surrogate keys in physical model; natural keys in logical
- Denormalize only when you have a performance requirement
3. Platform & Technology Selection
Decision tree:
- Need structured analytics + BI at scale? → Data Warehouse (Snowflake, BigQuery, Redshift)
- Need raw data + ML + flexible schemas? → Data Lake (S3 + Athena/Spark)
- Need both with ACID guarantees? → Lakehouse (Databricks, Iceberg, Hudi)
- Need domain ownership + federated governance? → Data Mesh (multiple warehouses/lakes per domain)
- Need real-time + low latency? → Streaming-first (Kafka + Flink + materialized views)
4. Governance & Compliance Planning
Governance pillars:
| Pillar | Activities | Tools |
|---|
| Data Quality | Profiling, validation rules, anomaly detection | dbt tests, Great Expectations, Monte Carlo |
| Data Catalog | Metadata, lineage, discovery | DataHub, Collibra, Alation |
| Access Control | RBAC, ABAC, masking, encryption | Platform-native + Immuta/Okera |
| Master Data Management | Golden records, deduplication | Informatica, Reltio, custom MDM |
| Compliance | GDPR, CCPA, HIPAA, SOC 2 | Legal review + technical controls |
