Curated for this specific career level.
Level path
Data Architecture Practitioner
Data Architect
Own features and services with solid implementation judgment across neighboring layers.
Concepts explicitly listed in this level.
Concepts span multiple tracks and layers.
Job description signalsTypical responsibilities↓
- Define data models, platform boundaries, integration patterns, governance, and lifecycle standards.
- Align analytical and operational data architecture with quality, privacy, security, and business needs.
- Guide data platform evolution, interoperability, ownership, discovery, and migration decisions.
- Own features end to end from design through implementation, testing, release, and support.
- Make local technical tradeoffs and explain them clearly to teammates.
- Improve reliability, maintainability, and observability of the systems you touch.
- Collaborate with product, design, QA, operations, and adjacent engineering teams.
Track / layer masteryConcept coverage by layer↓
Data
4 concepts
Software
1 concepts
Software
129 concepts
Data
39 concepts
Management
17 concepts
Operations
14 concepts
Product
8 concepts
Theory
3 concepts
Learning scopeConcepts by learning area↓
Specific to this level
Concepts introduced by this level
Level concepts
Data Platform Architecture
Design integrated warehouse, lake, lakehouse, and serving layers.
Level concepts
Data Governance
Establish ownership, discovery, quality, and controlled use of data assets.
Cumulative level expectation
All concepts expected at this level
Inherited path
Coding Foundations
Developers begin by learning to translate requirements into working, readable code.
Inherited path
Developer Tooling
Daily development depends on version control, language tooling, static checks, and repeatable local workflows.
Inherited path
Testing & Debugging
A developer owns not only writing code, but proving that it behaves as intended.
Inherited path
Employment Basics
Technical candidates benefit from understanding who employs them, how they are paid, and which terms govern the working relationship.
Inherited path
Programming Foundations
Learn the basic building blocks used to express behavior in code.
Inherited path
Software Design
Structure code so it remains readable, testable, and maintainable as it grows.
Inherited path
Data & Persistence
Understand how applications store, retrieve, and protect durable state.
Inherited path
Architecture & Systems
Design systems that handle growth, latency, failure, and multiple communicating components.
Inherited path
Production Awareness
Know how software is built, deployed, observed, and recovered in production.
Inherited path
Data foundations
Understand datasets, schemas, quality, SQL, and analytical storage basics.
Inherited path
Pipeline orchestration
Build and operate data movement and transformation workflows at scale.
Inherited path
Data platform production
Make data platforms reliable, discoverable, observable, and ready for analytics and ML consumers.
Inherited path
Data Modeling
Define consistent models, schemas, ownership, and data semantics.
Inherited path
Analytical Foundations
Understand the core structures used for analytical data.
Inherited path
Application Design
Mid-level developers own complete features and make local design choices inside an established architecture.
Inherited path
Runtime & Data
Feature ownership requires practical knowledge of runtimes, memory management, and relational persistence.
Inherited path
Delivery & Collaboration
Professional development includes the complete implementation loop, not only editing source files.
Inherited path
Data Platforms
Build the capabilities needed for this area.
Path concepts
Data Platform Architecture
Design integrated warehouse, lake, lakehouse, and serving layers.
Path concepts
Data Governance
Establish ownership, discovery, quality, and controlled use of data assets.