Rutvij S.

• Leading enterprise data transformation for AI-native spend management platform serving 3,500+ customers across $8T spend ecosystem.
• THE CHALLENGE
• Post-PE acquisition, Coupa had fragmented data infrastructure with no unified platform—AWS resources operating independently, 200+ duplicate reports, manual processes across Finance and Revenue Operations. Organization needed modern data capabilities but data wasn't accessible or trustworthy.
• MY APPROACH
• Led enterprise data transformation reporting to CIO. Built 28-person global team across Data Integration, Platform Engineering, Analytics, Data Science, and Governance. Consolidated fragmented infrastructure into unified Snowflake platform, designed architecture enabling cross-functional independence, operationalized self-service analytics for 800+ internal users.
• BUSINESS IMPACT
• Platform & Architecture
• Consolidated fragmented AWS infrastructure into unified Snowflake platform with medallion architecture
• Built raw data layer processing 40+ sources (99.9% availability, sub-15 minute SLAs)
• Reduced cross-functional dependencies 65% by consolidating 200+ duplicate reports into reusable data models
• Designed architecture positioning platform for data mesh adoption as organization matures
• Business Enablement
• Operationalized self-service analytics serving 800+ internal users across Finance, GTM, Product, Operations, HR, Legal, Compliance
• Finance: Integrated Anaplan, automated month-end close (60% faster), built real-time executive dashboards
• Revenue Operations: Unified 70-80% of enterprise data through Salesforce, enabled 95% forecasting accuracy, identified $12M+ at-risk deals quarterly
• Customer Success: Deployed ML for churn prediction (production) and propensity scoring (pre-production)
• PE Transformation & Governance
• Implemented Atlan data cataloging, automated lineage tracking, SOX compliance frameworks
• Partnered with C-suite through leadership transition

• Established enterprise BI practice from zero delivering real-time data warehouse.
• THE CHALLENGE: Dynatrace had zero BI capability. Business teams burned 400+ weekly hours creating manual Excel reports. Finance couldn't forecast ARR accurately. Sales lacked pipeline visibility. Product had no usage analytics. Leadership made decisions blind because data wasn't accessible.
• MY APPROACH: Built complete enterprise BI practice from zero in 12 months. Selected modern stack (Heroku, Snowflake, dbt, Power BI, Boomi), recruited global team, delivered production system. Started pragmatically — deployed Heroku for quick Salesforce analytics wins, then evolved to Snowflake lakehouse as volumes grew. Partnered with G&A, GTM, R&D to embed analytics into decision-making.
• BUSINESS IMPACT:
• Platform Engineering
• Built production platform evolving pragmatically: Heroku for rapid Salesforce analytics, then Snowflake lakehouse with dimensional modeling as data volumes scaled
• Engineered ELT pipelines with CDC patterns ingesting 40+ sources (sub-15 minute SLAs, 99.9% availability)
• Reduced compute costs 45% through incremental processing while maintaining reliability
• Business Analytics
• Finance: ARR automation (98% accuracy), eliminated 10-day manual close, built FP&A dashboards
• Marketing: Attribution modeling, campaign effectiveness
• Operations: Competitive win-loss analysis driving product strategy
• Automated 400+ weekly hours across functions
• Leadership & Partnership
• Partnered with G&A, GTM, R&D on strategic planning and executive briefings
• Established data quality standards adopted organization-wide
• Organizational Culture & Change Management
• Built high-performing organization emphasizing speed and delivery, establishing process frameworks balancing rapid iteration with quality.
• Built trust through consistent delivery and business acumen, becoming go-to advisor for data-driven decision making

• Led IT infrastructure transformation and data engineering modernization
• for enterprise data warehouse.
• Key Achievements:
• Led Azure cloud migration with Microsoft partnership and eventually to Snowflake
• Reduced ETL development cycles 40% through automated DevOps pipelines and cloud-native architectures
• Established operational excellence frameworks supporting Finance, Sales, Marketing, Merchandising analytics

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