Engineering Intelligence Lead

Role Summary

Own the Jellyfish platform, implementation and operation of engineering intelligence and delivery observability. Enable visibility into delivery lifecycle performance, engineering productivity, and workflow efficiency across AI initiatives.

Key Responsibilities

• Lead onboarding, configuration, and operationalization of Jellyfish platform
• Provide end-to-end visibility into delivery lifecycle and work orchestration
• Enable tracking of engineering productivity, work throughput and cycle time, delivery bottlenecks and inefficiencies
• Integrate Jellyfish with tools such as Azure DevOps (ADO), GitHub, Jira (future target)
• Design dashboards and reporting for leadership visibility
• Enable delivery governance
• Collaborate with AI DLC (Delivery lifecycle) teams, App Ops / Observability teams
• Support data-driven decision making for engineering leadership
• Enable scaling of the tooling ecosystem as additional tools are onboarded

Required Skills

• Strong experience with Jellyfish or similar engineering intelligence platforms
• Experience with SDLC tools integration (GitHub, ADO, Jira)
• Understanding of Agile delivery metrics
• Experience with software engineering lifecycle
• Experience building reporting dashboards and analytics frameworks
• Strong stakeholder management skills

Preferred Skills

• Exposure to AI/ML development lifecycle (AI DLC)
• Experience in observability and app performance measurement
• Familiarity with engineering productivity metrics frameworks