Engineering Intelligence / Jellyfish Platform Specialist
Job Title: Engineering Intelligence / Jellyfish Platform Specialist General Information Location: Chicago, IL / Minneapolis, MN Work Location: Remote Office Address: 7601 France Ave S, Suite 500, Minneapolis, MN 55435-5988 Duration: 6 months Education Start Date: 16-Sep-2026 Assignment Duration (as listed): 24 months Base Salary Range: $142,000-$150,000 Experience Range: 6-8 years Essential Skills: Engineering Intelligence, Jellyfish, Software Delivery Lifecycle (SDLC) Desirable Digital Skills: Microsoft Azure, GitHub Enterprise Education Requirements Bachelor's degree in Computer Science, Information Systems, or a related field. Equivalent professional experience may be considered. Role Description Lead the onboarding, configuration, and operationalization of the Jellyfish engineering intelligence platform. Provide end-to-end visibility into the software delivery lifecycle and work orchestration. Enable tracking and analysis of engineering productivity, throughput, cycle time, and delivery bottlenecks. Integrate engineering intelligence platforms with Azure DevOps, GitHub, and Jira. Design dashboards and reporting solutions for leadership visibility and delivery governance. Collaborate with AI Delivery Lifecycle (AI DLC), Application Operations (App Ops), and Observability teams. Support the scaling of the engineering tooling ecosystem as additional tools are onboarded. Must-Have Technical and Functional Skills Experience with Jellyfish or similar engineering intelligence platforms. Software Development Lifecycle (SDLC) tools integration, including: Azure DevOps (ADO) GitHub / GitHub Enterprise Jira Understanding of Agile delivery metrics and methodologies. Knowledge of the software engineering lifecycle. Experience developing reporting dashboards and analytics frameworks. Strong stakeholder management skills. Experience working with product and engineering teams. Ability to measure and analyze engineering productivity. Experience with delivery governance and reporting. Roles and Responsibilities Lead the onboarding, configuration, and operationalization of the Jellyfish platform. Provide end-to-end visibility into the delivery lifecycle and engineering work orchestration. Enable tracking of engineering productivity and performance metrics. Monitor work throughput, cycle time, and delivery efficiency. Identify delivery bottlenecks and inefficiencies. Integrate Jellyfish with Azure DevOps, GitHub, and Jira as required. Design and maintain dashboards for leadership reporting and delivery governance. Provide actionable insights to engineering leadership through analytics and reporting. Collaborate with AI DLC teams to support AI development lifecycle visibility. Work with App Ops and Observability teams to align delivery metrics with operational performance. Support the integration and scaling of additional tools within the engineering ecosystem. Enable data-driven decision-making across product and engineering organizations. Ensure delivery metrics and reporting provide meaningful insights into team performance and workflow efficiency. Preferred Technical Skills Exposure to the AI/ML Development Lifecycle (AI DLC). Experience with observability and application performance measurement. Familiarity with engineering productivity metrics frameworks. Knowledge of Microsoft Azure. Experience with GitHub Enterprise. Understanding of Agile delivery metrics, including cycle time, throughput, and workflow efficiency. Experience with engineering analytics and delivery observability. Generic and Managerial Skills Strong stakeholder management and communication skills. Ability to work directly with product and engineering teams. Strong analytical and problem-solving abilities. Experience supporting high-pressure delivery environments. Ability to coordinate across multiple technical teams. Strong reporting, presentation, and documentation skills. Ability to translate engineering metrics into actionable business insights. Keywords to Search in Resumes Jellyfish Engineering Intelligence Engineering Productivity Delivery Observability Azure DevOps (ADO) GitHub GitHub Enterprise Jira Agile Metrics Cycle Time Throughput Engineering Dashboards Delivery Governance AI DLC AI/ML Development Lifecycle Observability Application Performance Monitoring SDLC Software Delivery Metrics Engineering Analytics Workflow Optimization Pre-Screening Questionnaire What experience do you have implementing, configuring, or owning Jellyfish or similar engineering intelligence platforms? How have you integrated engineering intelligence tools with Azure DevOps, GitHub, or Jira? What dashboards or engineering metrics have you developed for delivery governance and leadership visibility? How have you measured engineering productivity, throughput, cycle time, and delivery bottlenecks? Do you have experience with AI/ML development lifecycle tools or observability platforms? Have you worked directly with product and engineering teams to improve delivery efficiency? Important Clarifications Duration discrepancy: The role lists a 6-month duration, while the assignment details specify 24 months. Confirm the actual contract duration with the recruiter. Salary clarification: Confirm whether the stated $142,000-$150,000 range represents annual base salary or another compensation structure. Work arrangement: The role lists Minneapolis as the office location but specifies remote work. Confirm whether any onsite attendance is required.