Dhruv Jethva

• Engineered robust data integration pipelines to consolidate disparate CRM, financial, and external
datasets, establishing a single source of truth for investor intelligence.
• Designed high-performance Power BI and Tableau dashboards, utilizing advanced semantic modeling to
reduce reporting turnaround time by 50%.
• Implemented automated data validation protocols to ensure the integrity and reliability of high-stakes
fundraising data used by senior leadership.
• Orchestrated automated reporting workflows, replacing manual data consolidation with streamlined
processes that enable real-time decision-making.
• Collaborated with investment teams to translate complex business strategies into precise technical KPI
definitions and data requirements.
• Deployed real-time alert systems to proactively identify high-value investment opportunities, directly
supporting the firms strategic agility.

• Architected end-to-end ETL/ELT pipelines using Data Factory (Gen2) to ingest large-scale engineering
datasets into a unified OneLake architecture.
• Developed and optimized PySpark notebooks within Microsoft Fabric to identify operational anomalies,
replacing legacy manual statistical analysis.
• Designed advanced Power BI semantic models (DirectLake mode) to support executive decision-making,
improving report performance and interactivity.
• Automated complex data transformation workflows using SQL Analytics Endpoints and stored
procedures, reducing manual reporting overhead by 20+ hours monthly.
• Collaborated with operations teams to define Data Governance standards, ensuring data quality across
the Lakehouse environment.
• Delivered actionable insights that improved pipeline efficiency by 15% by correlating historical
engineering data with real-time operational metrics.

• Collected, cleaned, and analyzed data from multiple sources, including SQL databases and APIs, to create
accurate reporting datasets.
• Designed and maintained dashboards in Power BI and Excel to track KPIs and provide insights for senior
management.
• Conducted exploratory data analysis (EDA) to identify trends, paterns, and anomalies, improving business
reporting accuracy by 20%.
• Partnered with business and analytics teams to build data models supporting performance measurement
and decision-making.
• Automated recurring reports using Python and Excel, reducing manual reporting effort by 25%.
• Delivered actionable insights and recommendations to stakeholders, aligning reporting with business
objectives.