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.