Spearheading Meta's transformation from broad data access to consent-first advertising via the Safe Ads Program

• Architected the end-to-end 8-stage privacy remediation pipeline (classification → lineage → topology sort → pipeline dev → diff → deploy → compliance → downstream migration)
• Mitigated 700+ data assets for AAP and Consent Revocation, achieving 100% remediation of all high-risk assets — directly enabling Meta's Safe Ads launch timeline
• Designed DP anonymization operators for safe analytics, preserving analytical capability during the consent transition
• Landed 300+ code changes and managed 1,000+ tasks; reduced average asset remediation cycle time from weeks to days
• Closed privacy gaps across cross-functional Dataswarm pipelines involving 3+ producer teams, preventing compliance audit failures

• Built an AAP remediation "Claude Code Skill" (YAML + Markdown playbook) that reduced new-member ramp-up from ~2 weeks to ~2 days
• AI-agent playbook cut remediation iteration time by 3–5x: auto-generated codemods, resolved multi-producer table alignment, replaced fragile metastore tracking
• Leveraged prompt engineering and LLM-assisted code analysis, reducing documentation overhead by ~70%

• Improved schema/dataset definitions, removed hardcoded paths, streamlined lint/schedule/test workflows — reducing production incidents
• Documented repeatable validation steps that cut time-to-first-deploy for new pipelines by ~50%
• Applied SQL/Spark-style ETL best practices sustaining 300+ change velocity over the engagement

Strategic architect for VMware's Partner Data Platform — unifying fragmented partner data into a single governed analytics layer

• Designed and built the Partner Data Platform from scratch, unifying 10+ disparate sources — eliminating data silos blocking cross-functional decisions for years
• Built data catalog and governance model (Python/Confluence) cutting tribal knowledge reliance by 60% and ad-hoc requests by ~40%
• Implemented VMware's first structured data governance for partner data — classification, lineage, policy enforcement — achieving audit-ready status
• Improved data consistency by 60% through standardized naming, automated quality checks, and cross-team contracts

• Pioneered an AI copilot for retrieval optimization and governance — one of VMware's earliest LLM-assisted data engineering deployments, reducing query dev time by ~30%
• Achieved 40%+ Spark processing time improvements through broadcast joins, caching, and skew management
• Established automated monitoring replacing reactive firefighting, reducing incident response from hours to minutes

• Drove alignment across 5+ data teams via shared roadmaps, reducing duplicate efforts by ~25%
• Led workshops upskilling 30+ partner data consumers, building organizational data literacy
• Engaged director/VP-level stakeholders, securing budget — team grew from 3 to 8 engineers

Led high-impact projects across Meta's Ads, Commerce, and Privacy teams

• Architected centralized DW unifying product/seller data — reducing decision-making cycle from weeks to days
• Launched Category Management Data Warehouse enabling cross-vertical analysis and measurable GMV growth
• SMB funnel redesign: 3x query performance improvement, scaling to tens of thousands of advertisers

• Led privacy remediation across 1,200+ SMB 2.0, Customer Journey, and BPO tables — establishing patterns adopted in Safe Ads
• Enhanced anonymization with Hive engineers, reducing audit preparation effort by ~60%
• Drove Salesforce ID deprecation across 1,000+ pipelines, eliminating vendor dependency affecting ~15% of joins

• Migrated 1,000+ Hive pipelines to Spark, automating ~70% — cut projected 12-month migration to under 6 months
• Built chargeback/leakage dashboards (FGF, Dataswarm, Unidash) identifying previously undetected revenue leakage
• Designed Marketplace App reliability dashboard — reducing MTTR for payment incidents via real-time visibility

• Migrated 1,000+ Hive pipelines to Spark; built chargeback representment & Marketplace reliability dashboards

• Led GDPR compliance: encrypted sensitive data into Dali storage using Hive, Python, and Pig; retired legacy sources

• 80+ Dataswarm pipelines for petabyte-scale ads: Cross Device Insights, Global Account Pipeline, Outcomes Datamart + Norms DB, Facebook Media & Live Monetization

• PII masking, Vertica→Redshift migration, 1,000+ table optimization

• Life Sciences BI, cloud migration, ETL modularization

• Incentive comp analytics, BusinessObjects XI

• Enterprise DW + 8 datamarts; ETL reduced from 18 hrs to 3 hrs; 40–50% sales lift via clickstream analytics