Skill Profile

Edge Computing for IoT

Edge computing: AWS Greengrass, Azure IoT Edge, fog computing, local inference

Embedded & IoT IoT Platforms

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 2 optional

Embedded & IoT

IoT Platforms

3/17/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
IoT Engineer		Understands edge computing concept for IoT: processing data on device instead of sending to cloud. Runs simple filters and aggregators on ESP32/Raspberry Pi.

Role	Required	Description
IoT Engineer		Deploys edge computing on IoT gateways: AWS Greengrass, Azure IoT Edge. Implements local data processing and filtering. Synchronizes edge-cloud state.

Role	Required	Description
IoT Engineer	Required	Designs edge computing architecture for IoT: multi-tier (device → gateway → cloud). Implements ML inference on edge (TensorFlow Lite, ONNX). Enables OTA edge model updates.

Role	Required	Description
IoT Engineer	Required	Defines edge computing strategy: what to process on edge vs cloud. Establishes deployment and update standards for edge applications across the IoT device fleet.

Role	Required	Description
IoT Engineer	Required	Defines enterprise edge computing strategy for IoT. Evaluates KubeEdge, OpenYurt for edge cluster orchestration. Builds edge AI roadmap for industrial IoT.

Junior 1 requirements

IoT Engineer

Understands edge computing concept for IoT: processing data on device instead of sending to cloud. Runs simple filters and aggregators on ESP32/Raspberry Pi.

Middle 1 requirements

IoT Engineer

Deploys edge computing on IoT gateways: AWS Greengrass, Azure IoT Edge. Implements local data processing and filtering. Synchronizes edge-cloud state.

Senior 1 requirements

IoT Engineer
Required

Designs edge computing architecture for IoT: multi-tier (device → gateway → cloud). Implements ML inference on edge (TensorFlow Lite, ONNX). Enables OTA edge model updates.

Lead / Staff 1 requirements

IoT Engineer
Required

Defines edge computing strategy: what to process on edge vs cloud. Establishes deployment and update standards for edge applications across the IoT device fleet.

Principal 1 requirements

IoT Engineer
Required

Defines enterprise edge computing strategy for IoT. Evaluates KubeEdge, OpenYurt for edge cluster orchestration. Builds edge AI roadmap for industrial IoT.

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