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.