Remote Operations Analytics Lead: Data-Driven Decisions

Jobgether

United States

Remote

USD 130,000 - 160,000

Full time

6 days ago

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Benefits offered by this job

Medical, dental, vision

401(k) with employer match

Remote work within the United States

Wellness programs

Life & disability insurance

Job summary

Jobgether is seeking an Operations Analytics Lead based in the United States. This fully remote, strategic role sits at the intersection of operations, analytics, data, and AI to translate insights into measurable business outcomes.

You will own an analytics roadmap, partner with data and engineering, and drive adoption of analytics solutions to improve workflows. The ideal candidate has 8+ years of experience in data ownership or analytics, strong operational analytics expertise, and practical

Qualifications

8+ years in data product ownership, analytics, data subject matter expertise, or a comparable role with measurable outcomes.
Operational analytics experience navigating complex data environments and extracting relevant information.
Ability to translate data analyses into actionable business recommendations.
Strong cross-functional collaboration with centralized data and engineering teams.
Excellent communication and influence to senior stakeholders.
Practical understanding of AI and agentic technologies and their operational implications.
Familiarity with data tooling and Snowflake to communicate with technical teams.
Experience in regulated industries such as insurance or financial services is preferred.

Responsibilities

Own the analytics portfolio by translating strategy into a prioritized roadmap of data, analytics, and AI opportunities.
Turn data into decisions by interpreting operational data and presenting recommendations for leadership.
Drive adoption by embedding insights into real workflows and measuring value.
Partner with data and engineering teams to define requirements, data dependencies, success criteria, and acceptance standards.
Shape the operational AI agenda by identifying practical opportunities and governance considerations.
Prioritize meaningful outcomes and focus resources on high-value problems with measurable impact.
Build cross-functional partnerships across operations, data, engineering, and leadership.
Support continuous improvement by investigating data discrepancies and identifying optimization opportunities.