Job Details
Skills: Jellyfish, Engineering Intelligence, Azure DevOps, GitHub Enterprise, Jira, Agile Metrics, Delivery Observability
Role Overview
We are seeking an experienced Engineering Intelligence Platform Lead to drive the onboarding, configuration, integration, and operationalization of the Jellyfish platform or a similar engineering intelligence solution. The role will focus on providing end-to-end visibility into the software development lifecycle (SDLC), engineering productivity, delivery performance, and work orchestration.
The ideal candidate will have hands-on experience integrating engineering intelligence platforms with tools such as Azure DevOps, GitHub Enterprise, and Jira, along with expertise in Agile delivery metrics, reporting dashboards, and stakeholder management. Exposure to AI/ML development lifecycle (AI DLC) and application observability is highly desirable.
Key Responsibilities
- Lead the onboarding, configuration, implementation, and operationalization of the Jellyfish platform or similar engineering intelligence tools.
- Establish end-to-end visibility into the software delivery lifecycle, engineering workflows, and work orchestration.
- Enable tracking and analysis of engineering productivity, work throughput, cycle time, delivery bottlenecks, and process inefficiencies.
- Integrate engineering intelligence platforms with Azure DevOps (ADO), GitHub Enterprise, and Jira.
- Design and develop dashboards, reports, and analytics frameworks to support engineering leadership, delivery governance, and data-driven decision-making.
- Establish and monitor Agile delivery metrics and engineering performance indicators.
- Partner with product management, software engineering, AI DLC, Application Operations (App Ops), and Observability teams to improve delivery visibility and operational effectiveness.
- Identify opportunities to improve engineering workflows, delivery efficiency, and software development practices.
- Support the scaling of the engineering intelligence ecosystem as additional tools, teams, and data sources are onboarded.
- Ensure stakeholders have actionable insights into delivery health, productivity trends, and potential risks.
- Support high-pressure delivery readiness activities and communicate progress, risks, and recommendations to leadership.
Must-Have Technical and Functional Skills
- Hands-on experience implementing, administering, or owning Jellyfish or a comparable engineering intelligence platform.
- Strong understanding of the software development lifecycle (SDLC) and Agile delivery methodologies.
- Experience integrating engineering tools and data sources, particularly Azure DevOps, GitHub Enterprise, and Jira.
- Knowledge of engineering productivity and delivery metrics, including cycle time, throughput, work in progress, and delivery bottlenecks.
- Experience developing reporting dashboards, analytics frameworks, and leadership-level delivery reports.
- Ability to translate engineering data into actionable insights for delivery governance and continuous improvement.
- Strong stakeholder management and cross-functional collaboration skills.
Preferred Skills
- Exposure to AI/ML development lifecycle (AI DLC) processes and tooling.
- Experience with application observability, performance monitoring, and delivery observability.
- Familiarity with engineering productivity measurement frameworks and software delivery performance indicators.
- Experience working with App Ops, platform engineering, or engineering enablement teams.
- Understanding of data-driven engineering governance and tooling ecosystem optimization.