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Senior Support Operations Analyst ( Power BI / Microsoft Fabric)

Modeln

Hyderabad India Hybrid permanent

Posted: May 8, 2026

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Quick Summary

We are seeking a Senior Support Operations Analyst to translate complex support activity into clear, actionable insight. The ideal candidate should have a strong background in data analysis, with expertise in Power BI and Microsoft Fabric, and a passion for technical writing. A high level of technical proficiency and strong analytical skills are required.

Job Description

We are seeking a Senior Support Operations Analyst to help translate complex support activity into clear, actionable insight. This role sits at the center of a highly technical, engineer-led support organization and is responsible for making support work visible, understandable, and useful to decision makers across Support, Product, and Customer Success. Unlike traditional support analytics roles focused on volume and efficiency, this role emphasizes signal over noise—identifying patterns in nuanced issues, shaping meaningful metrics, and ensuring support data reflects real product and customer challenges. The ideal candidate is comfortable working with incomplete or imperfect data, enjoys bringing structure to ambiguity, and can connect operational data to broader business impact. This role reports to the Manager of Support Operations and will partner closely with Support leadership, Product, Engineering, and Customer Success.


Job Responsibilities:
Metrics & Reporting

• Define, refine, and operationalize a set of support metrics that reflect both operational health and customer impact (not just volume and SLA)

• Build and maintain dashboards across the Microsoft Fabric ecosystem to provide visibility into:

o Support demand and trends

o Backlog health and case lifecycle

o Escalations and recurring issue patterns

• Evaluate the effectiveness of AI-enabled support capabilities, identifying where outputs are inaccurate, misleading, or fail to support complex support scenarios.

Insights & Analysis

• Analyze support data to identify systemic product issues, friction points, and emerging risks

• Translate complex support activity into clear narratives and recommendations for Product and Engineering

• Partner with Customer Success (Gainsight) to ensure support data contributes meaningfully to:

o Health scores

o CSAT analysis

o Customer risk identification

Data Quality & Structure

• Partner with Support Ops and System Admin to improve:

o Case taxonomy

o Categorization accuracy

o Data completeness and usability

• Identify gaps in data capture and propose pragmatic improvements

Cross-Functional Enablement

• Act as a key partner to Support, Product, and CS teams in understanding support trends

• Support regular business reviews (weekly/monthly) with clear, insight-driven reporting

• Help establish feedback loops between support insights and product improvements

AI & Emerging Capabilities

• Evaluate outputs from AI-enabled tools (e.g., Forethought, Agentforce) to:

o Assess accuracy and usefulness

o Identify opportunities to improve knowledge capture and insights

• Partner with Ops and Systems to ensure AI-generated data is measurable and trustworthy


Job Qualification:
• 5–7 years of experience in Support Operations, Business Analytics, or similar roles in a B2B environment

• Strong experience with BI tools within the Microsoft Fabric ecosystem (Power BI, etc.)

• Experience working with Salesforce Service Cloud data models

• Demonstrated ability to work with ambiguous or incomplete datasets

• Experience partnering with Product, Engineering, or Customer Success teams

• Familiarity with Gainsight or similar CS platforms preferred

• Exposure to AI-enabled support tools (e.g., Forethought, Agentforce) is a strong plus

• Strong analytical and storytelling skills—ability to turn data into decisions

What Success Looks Like

• Clear, trusted visibility into support trends and performance

• Support data is actively used to inform product and customer decisions

• Improved alignment between Support, Product, and Customer Success

• Meaningful metrics that reflect real customer experience, not just operational proxies

• High confidence in data quality and reporting consistency

• Clear Metrics for measuring AI effectiveness, and drive for improving those outcomes.

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