Domain
Machine Learning & AI
Skill Profile
Feast, Tecton, online/offline stores, feature engineering pipelines, versioning
Roles
3
where this skill appears
Levels
5
structured growth path
Mandatory requirements
11
the other 4 optional
Machine Learning & AI
MLOps
3/17/2026
Choose your current level and compare expectations. The items below show what to cover to advance to the next level.
The table shows how skill depth grows from Junior to Principal. Click a row to see details.
| Role | Required | Description |
|---|---|---|
| Data Scientist | Retrieves features from existing feature stores for model training. Understands the concept of feature reuse and online/offline serving. Follows team conventions for feature naming and versioning. | |
| ML Engineer | Required | Understands feature store concept: online vs offline store, feature reuse. Reads features from Feast for training. Understands feature freshness and consistency. |
| MLOps Engineer | Deploys and monitors feature store infrastructure components. Understands data ingestion pipelines feeding the feature store. Performs basic troubleshooting of feature freshness and availability issues. |
| Role | Required | Description |
|---|---|---|
| Data Scientist | Engineers and registers new features in the feature store with proper documentation. Implements feature pipelines with point-in-time correctness to prevent data leakage. Evaluates feature importance and manages feature lifecycle for experiments. | |
| ML Engineer | Required | Configures Feast for the project. Defines feature definitions (entities, feature views). Configures materialization for online store. Integrates feature store with training pipeline. |
| MLOps Engineer | Configures feature store platforms like Feast or Tecton for online and offline serving. Builds automated pipelines for feature computation, validation, and backfilling. Monitors feature drift and data quality metrics in production. |
| Role | Required | Description |
|---|---|---|
| Data Scientist | Required | Designs feature store architecture enabling cross-team feature sharing and discovery. Defines feature governance policies including versioning, deprecation, and access control. Mentors teams on feature engineering best practices and scalable feature pipelines. |
| ML Engineer | Required | Designs feature store architecture. Optimizes materialization for large volumes. Configures streaming feature computation. Ensures feature consistency between training and serving. |
| MLOps Engineer | Required | Architects enterprise feature store platforms supporting real-time and batch serving at scale. Designs feature pipelines with streaming ingestion and low-latency retrieval for production models. Mentors teams on feature store operations, cost optimization, and reliability. |
| Role | Required | Description |
|---|---|---|
| Data Scientist | Required | Defines Feature Stores strategy at team/product level. Establishes standards and best practices. Conducts reviews. |
| ML Engineer | Required | Defines feature store strategy for the organization. Evaluates Feast vs Tecton vs custom solution. Designs feature governance and discovery. |
| MLOps Engineer | Required | Defines the Feature Store strategy at team/product level. Establishes standards and best practices. Conducts reviews. |
| Role | Required | Description |
|---|---|---|
| Data Scientist | Required | Defines Feature Stores strategy at organizational level. Establishes enterprise approaches. Mentors leads and architects. |
| ML Engineer | Required | Defines feature engineering strategy for enterprise. Designs feature platform. Evaluates novel approaches to feature management. |
| MLOps Engineer | Required | Defines the Feature Store strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects. |