Spearheading Meta's transformation from broad data access to consent-first advertising

Privacy Remediation & Compliance Engineering

• Operated within a 20-person cross-functional team (8 data engineers, 12 software engineers) facing a 6-month regulatory deadline to remediate 4,000+ tables/assets — initial manual approach surfaced systemic issues: incorrect classifications, pipeline failures, and unscalable ad-hoc workflows
• Executed the end-to-end 8-stage remediation pipeline (classification → lineage → topology sort → pipeline dev → diff → deploy → compliance → downstream migration) across 700+ personally mitigated assets, helping the team convert a fragile manual process into a repeatable, systematic workflow
• Achieved 100% high-risk asset remediation for AAP and Consent Revocation — directly unblocking Meta's Safe Ads launch timeline and enabling downstream teams to resume development on compliant data
• Contributed to AI-accelerated surge-readiness that achieved ~98% overall remediation burndown, delivered ~21 engineering-year-equivalent impact, and pulled delivery forward ~6 months
• Landed 300+ code changes, 1,000+ tasks; established a triage framework that reduced per-asset cycle from ~1.86 → ~0.53 weeks (71.5% reduction) — enabling the team to absorb expanding scope without schedule slippage
• Closed privacy gaps across Dataswarm pipelines involving 3+ producer teams — resolving multi-producer DAG dependencies and preventing compliance audit failures

AI-Assisted Remediation & Claude Code Skill

• Championed the team's pivot from manual to AI-assisted remediation after early manual sprints revealed classification errors and pipeline rework that threatened the 6-month deadline
• Built an AI-assisted remediation "Claude Code Skill" (YAML + Markdown) that standardized how the 20-person team creates, debugs, and tests remediation pipelines — contributing to the 71.5% unit-effort reduction
• Codified best practices (consent-filtered datasets, naming conventions, critical rules, workflows, troubleshooting) that reduced classification errors and rework
• Validated and "dogfooded" an AI remediation assistant, strengthening workflow robustness through schema/dataset hygiene, safer configuration standards, and repeatable lint/schedule/test validation

DataSwarm Pipeline Engineering & Operational Quality

• Strengthened DataSwarm pipeline structure — improved schema/dataset definitions, removed hardcoded paths, streamlined lint/schedule/test workflows — establishing patterns the full team adopted
• Documented repeatable validation steps that accelerated onboarding for new engineers joining the surge effort, reducing time-to-first-deploy and minimizing production rollbacks
• Applied SQL/Spark-style ETL best practices and automated checks that improved debuggability and consistency, sustaining the team's 300+ code-change velocity

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

Data Architecture & Platform Unification

• Designed and built the Partner Data Platform from the ground up, unifying fragmented partner data from 10+ disparate sources into a single governed analytics layer — eliminating data silos that had blocked cross-functional decision-making for years
• Designed an intuitive data catalog and governance model (Python/Confluence) that drove broad self-service adoption, cutting reliance on tribal knowledge by 60% and reducing ad-hoc data request volume by ~40%
• Implemented VMware's first structured data governance for partner data — classification, lineage tracking, and policy enforcement — achieving audit-ready status
• Improved data consistency by 60% through standardized naming conventions, automated quality checks, and cross-team data contracts

AI & Performance Engineering

• Pioneered an AI copilot for retrieval optimization, coding, and governance best practices — one of VMware's earliest LLM-assisted data engineering deployments, reducing routine query development time by ~30%
• Achieved 40%+ Spark processing time improvements through broadcast joins, caching strategies, and skew management — enabling SLA-critical reports to complete within delivery windows for the first time
• Established automated monitoring and proactive alerting that replaced reactive firefighting, reducing pipeline incident response time from hours to minutes

Leadership & Cross-Functional Alignment

• Drove cross-team alignment across 5+ data teams via shared roadmaps, reducing duplicate pipeline development efforts by ~25%
• Led workshops and knowledge-sharing sessions that upskilled 30+ partner data consumers, building organizational data literacy
• Engaged director/VP-level stakeholders to define a unified data vision, securing budget and headcount — team grew from 3 to 8 engineers

Led high-impact projects across Meta's Ads, Commerce, and Privacy teams, architecting secure, scalable, and compliant data platforms

Commerce Data Architecture

• Architected a centralized data warehouse that unified product and seller data into a single analytics layer — enabling commerce teams to reduce decision-making cycle time from weeks to days
• Designed and launched the Category Management Data Warehouse, providing deep cross-vertical analysis that empowered commerce teams to identify underperforming seller segments, contributing to measurable GMV growth
• Owned the SMB funnel redesign, improving query performance by 3x while maintaining key functionality

Privacy Engineering & Compliance

• Led privacy remediation across 1,200+ SMB 2.0, Customer Journey, and BPO tables — resolving critical design flaws and establishing remediation patterns later adopted at scale in the Safe Ads program
• Collaborated with Hive Anonymization engineers to enhance anonymization processes, reducing audit preparation effort by ~60%
• Drove Salesforce ID deprecation and Facebook ID integration across 1,000+ pipelines, eliminating an external vendor dependency that had caused data consistency issues affecting ~15% of cross-system joins

Data Platform Migration

• Spearheaded migration of 1,000+ Hive pipelines to Spark, automating ~70% of conversion tasks — reducing what was projected as a 12-month migration to under 6 months
• Built chargeback representment and leakage dashboards (FGF, Dataswarm, Unidash) that identified previously undetected revenue leakage, enabling the finance team to recover funds
• Designed Marketplace App reliability dashboard covering payin/payouts and user stability — reducing MTTR for payment-related incidents

• Led GDPR compliance efforts: encrypted sensitive customer data into LinkedIn Dali storage using Hive, Python, and Pig; retired legacy data sources and feeds; migrated to a stable, compliant framework

• Developed 80+ complex Dataswarm pipelines for petabyte-scale ads data across Sales Operations and Marketing Intelligence: Cross Device Insights, Global Account Pipeline, Gaming Apps Reporting, Outcomes Datamart + Norms DB, Facebook Media & Live Monetization
• Deep expertise in petabyte-scale ads data, partnering with cross-functional teams to optimize revenue generation strategies and deliver scalable analytics

• 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