What are the responsibilities and job description for the Director of Engineering position at EdgeX?
Company Overview
EdgeX provides an end to end system for environmental monitoring and indoor asset tracking in healthcare and regulated environments. The platform combines IoT devices, cloud data services, and analytics to deliver real time operational data.
A dedicated hardware team owns device design and production. The cloud platform ingests, processes, and exposes data from deployed devices operating in active clinical environments with strict uptime and compliance requirements.
Role Overview
The Director of Engineering leads the cloud engineering organization. Scope includes device to cloud ingestion, data pipelines, APIs, and customer facing systems.
This is a player manager role. You lead teams and also contribute directly to architecture, debugging, and critical implementations.
You are responsible for system behavior in production, including latency, data integrity, and fault handling.
Key Responsibilities
- Lead and grow engineering teams across platform services, APIs, and data pipelines.
- Act as a player manager, contributing directly to architecture, debugging, and critical code paths.
- Own device to cloud ingestion architecture and time series data systems.
- Define and enforce interface contracts between hardware and cloud systems.
- Design real time processing and low latency query systems.
- Ensure observability using tools such as Datadog, Grafana, Prometheus, or Azure Monitor.
- Build fleet scale systems for provisioning, monitoring, and remote management.
- Work directly with hardware teams to resolve field issues.
- Drive engineering standards across CI CD, security, testing, and incident response.
- Collaborate with product and hardware teams to align platform capabilities with customer use cases and roadmap priorities
- Operate under tight timelines and production constraints.
Requirements
- 7 to 10 plus years of engineering leadership experience.
- Hands on player manager experience contributing directly to technical work.
- Experience with cloud platforms for IoT or telemetry systems.
- Experience with real time ingestion pipelines and time series systems.
- Experience with Azure data services such as Event Hubs, IoT Hub, Data Factory, Synapse, or Log Analytics.
- Experience with Azure Data Explorer or similar time series platforms.
- Experience with observability tools such as Datadog, Grafana, Prometheus, or Azure Monitor.
- Strong understanding of distributed systems and failure handling.
- Experience debugging production systems with real world data.
- Ability to work closely with hardware teams.
AI Requirements
- Experience using AI tools to improve engineering productivity such as coding, testing, and debugging.
- Working knowledge of LLM systems including prompts, embeddings, and retrieval approaches.
- Ability to evaluate where AI adds value versus deterministic approaches.
- Understanding of model behavior and failure modes in production systems.
- Ability to integrate AI into workflows without degrading system reliability.
Preferred
- Experience in healthcare or regulated environments.
- Experience with IoT, asset tracking, or environmental monitoring.
- Experience with high availability systems and strict up