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data-architect

Data & analytics

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Updated 10-16 04:56

About this skill

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

  1. Identify the user's data architecture need (platform choice, modeling, governance, or decision framework)
  2. Follow the corresponding workflow below
  3. 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:

  1. Define the decision context

    • Business drivers (scale, latency, cost, compliance)
    • Constraints (budget, timeline, existing tech, team skills)
    • Stakeholders (data engineers, analysts, product, legal)

  2. Identify alternatives

    • At least 3 options (do nothing, minimal change, transformative)
    • Include cloud-native, hybrid, and open-source alternatives

  3. 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

  4. Assess risks & mitigation

    • Migration risk, talent risk, operational risk
    • POC plan for the top 2 options

  5. 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