Sneha Susan Varghese

• Owned & developed contact center data products, architecture supporting analytics, reporting, & operational decision-making across enterprise stakeholders.
• Led implementation of a third-party speech analytics product, defining product requirements and configuring 300+ metadata elements and KPIs to ensure consistent data capture, standardization, and downstream usability.
• Developed and implemented a dynamic agent ranking algorithm leveraging business performance metrics independent of interaction volume, increasing performance transparency, improving incentive alignment, and driving a 20% uplift in productivity and 15% improvement in KPI attainment across the contact center.
• Designed and operationalized data pipelines from Snowflake to external vendors, enabling reliable and scalable data consumption for analytics use cases.
• Provided product and domain leadership for an in-house data lake initiative, defining data models, entity relationships, and mappings between UI elements and backend Snowflake fact tables.
• Established data governance and quality frameworks using Python to improve data consistency and trust across non-integrated downstream systems.
• Collaborated with stakeholders during mergers and new module launches to identify critical data requirements and mapped data flow with the addition of new modules.
• Delivered interactive dashboards and insights (Tableau, Dossier) to surface call driver analysis, performance trends, and operational risks.
• Designed and implemented a data-driven call volume segmentation framework aligning advisor tiers to call intent and SLA requirements, optimizing routing and workload distribution to improve SLA adherence, advisor utilization, and overall operational efficiency.

Assisted faculty with database course classroom of 150+ students with instructional materials development and grading.

• Supported enterprise clients by translating business and functional requirements into testable data and system scenarios.
  • Contributed to API-based data access by implementing REST API integrations and parsing JSON responses to support downstream validation and ensuring 100% analytics needs are met

• Translated client and business requirements into reusable automation components, improving test coverage and execution efficiency to a 99%
  Verified the System Integration covers functional and data requirements