Technical Operational Manager
Inactive
Based in IndiaFull-TimeManager
This job is no longer active. We keep the page for reference, but the employer may not accept new applications.
SalaryCompetitive compensation with performance-based bonus eligibility
Job Details
- Experience
- 5+ years
- Required Skills
- Agile
Requirements
- 5+ years of experience in Engineering Operations, Program Management, or similar data-intensive engineering roles.
- Expert-level proficiency in Jira Cloud administration, including workflows, permissions, and automation.
- Strong working knowledge of Jellyfish or similar engineering intelligence tools.
- Deep understanding of Agile delivery metrics and engineering performance indicators, including DORA and flow metrics.
- Strong ability to design and manage structured engineering data systems, taxonomies, and reporting frameworks.
- Experience using AI tools and LLMs to automate analysis, reporting, and operational workflows.
- Basic scripting or integration experience using Python or JavaScript for API-based automation is a plus.
- Familiarity with SDLC, Scrum, and scaled Agile frameworks such as SAFe.
- Strong analytical thinking, communication skills, and ability to influence cross-functional stakeholders.
Responsibilities
- Design, configure, and maintain scalable engineering workflows, issue structures, and automation rules across large-scale Jira environments.
- Map engineering work data into investment and delivery insights using Jellyfish, ensuring accurate representation of product and engineering allocation.
- Standardize issue taxonomy, workflows, and data governance across multiple engineering squads to ensure consistent reporting and analytics.
- Build and maintain automation systems for ticket hygiene, workflow optimization, and operational efficiency using AI agents and rule-based systems.
- Develop and deliver engineering metrics dashboards focused on DORA and flow metrics such as cycle time, lead time, deployment frequency, and bottlenecks.
- Partner with finance and leadership teams to support capital allocation, R&D reporting, and engineering investment visibility.
- Translate complex engineering datasets into executive-level insights, narratives, and recommendations using AI-assisted analysis tools.
- Collaborate with stakeholders across engineering, product, and operations teams to continuously improve operational frameworks and reporting accuracy.