Kushal Sinh Vaghela

Business Technology professional with 2+ years of experience building data pipelines, data warehousing, and reporting solutions in the pharmaceutical sector. Skilled in SQL, Python, and cloud platforms, with hands-on expertise in ETL development and optimization, integrating U.S. pharmaceutical claims datasets (Komodo, LAAD) and CRM activity data (Veeva) to power HCP targeting, segmentation, and Next Best Action recommendations. Experienced in translating business requirements into technical solutions, automating analytics workflows, and communicating findings to onshore and client stakeholders.

Strong background in data modeling, issue analysis, and end-to-end delivery, with a track record of measurable improvements in system efficiency and commercial impact.

Business Technology Solutions Associate at ZS Associates (2024-07 – Present)

• Translated business and client requirements into technical solution design, leading development of GenAI-powered Next Best Action data assets that synthesize real-world claims, CRM activity, and specialty pharmacy data into structured HCP insights - driving a 15% lift in field insight adoption across 70+ reps.
• Optimized ETL and processing performance, reducing GenAI summary generation runtime by ~90% (full-day cycle to under 1 hour) through prompt optimization and tokenization, enabling near real-time insight delivery to field teams.
• Designed competitor-based KPIs from U.S. pharmaceutical datasets (Komodo, LAAD claims data) to surface competitive prescribing signals, powering targeted alerts within GenAI-generated HCP summaries.
• Built HCP segmentation logic classifying prescribers by prescribing behavior - writer status, loyalty, and tier - to identify and prioritize high-value targets across the Rare Disease & Neurology portfolio.
• Led a cross-functional team of 4, owning sprint planning, code reviews, QA, and mentorship, while serving as a key technical contact for onshore and client stakeholders across delivery cycles.
• Owned end-to-end delivery of 4 production data pipelines across the Rare Disease & Neurology portfolio, managing timelines and ensuring data accuracy, reconciliation integrity, and seamless downstream CRM delivery.
• Evaluated and optimized ETL processes, restructuring refresh dependencies and processing logic to accelerate cycles and save 2–3 hours per cycle, improving overall system efficiency.
• Conducted root-cause issue analysis on upstream data issues, schema drifts, and pipeline failures, implementing fixes and automated monitoring that strengthened pipeline resilience.
• Automated data validation, QC, and refresh workflows in Python and SQL, eliminating 3+ hours of manual effort per cycle across all pipelines.
• Architected a CAPA-based data warehouse and reporting solution for rare disease brands, delivering 3 report types per brand on daily, monthly, and ad-hoc cadences to Medical, Safety, and Data Management stakeholders.
• Synthesized findings into recommendations, identifying and flagging data quality gaps across patient-level journeys and driving upstream corrections at the client end to improve downstream reliability.
• Streamlined end-to-end report generation and distribution, enabling zero-touch fixed-cadence delivery and eliminating reporting lag at source.

Bachelor of Engineering in Computer Science & Engineering – Chandigarh University (2020-08 – 2024-06)