Pramod Gaddampally
Senior Engineering Manager, Data Engineering (Associate Director) @KINESSO
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WORK HISTORY
Senior Engineering Manager, Data Engineering (Associate Director) @KINESSO
San Francisco, CA, US
Own end-to-end data engineering strategy for cloud analytics platforms (AWS + Azure + Snowflake), defining architecture, tooling, and standards that align a multi-year technical roadmap with omnichannel retail, media, and product priorities.• Architected a metadata-driven ELT framework and unified cross-channel campaign-performance pipelines (DSP, CRM, Finance, web analytics), enabling full-funnel attribution, closed-loop measurement, and more precise targeting — eliminating 8–15 hours/week of manual reconciliation.• Built canonical impression/click measurement and attribution-ready clickstream layer, reducing metric discrepancies by 20–40% and downstream funnel breaks by 15–30% for 100+ analysts and business stakeholders.• Re-platformed orchestration onto Dagster with monitoring, alerting, and automated incident workflows — improving SLA adherence by ~30% while reducing firefighting effort by 5–10 hours/week.• Drove 25–35% Snowflake compute savings through warehouse right-sizing, query/model tuning, and smarter workload scheduling — without sacrificing SLA commitments.• Built standardized asset library (dbt + Dagster templates, runbooks, engineering guides) that cut new-hire onboarding time by ~50% and reduced early-stage defects by 10–20%.• Lead and mentor a distributed engineering team; established CI/CD pipelines, secure coding standards, code review practices, and documentation frameworks that increased release frequency and reduced production incidents.
EDUCATION
San José State University
Master's degree, Industrial And Systems Engineering
SVIT, Vasad Official
Bachelor's degree, Mechanical Engineering
ABOUT PRAMOD GADDAMPALLY
I build data platforms that organizations can actually trust — reliable, cost-efficient, and engineered for scale.Over 14 years across AdTech and Healthcare, I\'ve led teams that transformed fragile, siloed data ecosystems into governed, high-performance platforms. At Kinesso, I\'m currently owning cloud data strategy (AWS + Azure + Snowflake) for omnichannel retail and media analytics. At Veeva Systems, I spent 8 years building enterprise data infrastructure for pharma commercial and clinical teams.Here\'s what I\'ve delivered: 70% reduction in platform downtime — led full modernization from legacy Redshift to Spark + Iceberg 65–80% faster pipeline runtimes — Spark/Iceberg adoption with partitioning, clustering, and job tuning 25–35% Snowflake cost savings — warehouse right-sizing, workload scheduling, and spend governance ~30% better SLA adherence — re-platformed orchestration onto Dagster with monitoring + alerting 50% faster team onboarding — built standardized asset libraries and engineering enablement programsWhat makes me different from most data engineering leaders:I build reusable systems, not one-off pipelines. Whether it\'s a metadata-driven workflow framework that scales delivery across 30+ engineers, or a canonical measurement layer that gives 100+ analysts a single source of truth — I invest in infrastructure that multiplies team output.I bridge measurement integrity and platform reliability. Most leaders focus on one or the other. I\'ve built canonical impression/click datasets AND engineered the platform reliability underneath them.I\'ve done it in two demanding verticals. AdTech measurement (DSP/CRM/Finance unification, attribution-ready clickstream layers) and Healthcare data (CRM/EHR/claims/clinical integration, pharma regulatory data models). Each requires precision, governance, and stakeholder alignment at scale.Tech stack: Snowflake, AWS Redshift, Spark, Apache Iceberg, Python, dbt, Dagster, SQL, Azure, Tableau, Sigma
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