• Landed 1,000+ production changes across a multi-year privacy migration — sustained solo throughput normally requiring a team of 3–4 engineers
• Cleared the final blocker to a company-wide data warehouse consolidation program. My workstream covered the last ~20% of warehouse data — the portion nobody else could touch because it sat directly in the advertising revenue path. Completing it took the program from 80% to 100% coverage and enabled its formal close-out
• Executed that migration with zero revenue impact on the highest-sensitivity assets in the warehouse, under a constraint that any error would surface as ads revenue loss rather than a test failure
• Proved the pre-deployment validation stack was structurally blind — permission checks returned PASS for tables that didn't exist and never modeled runtime enforcement. Built the hard-gate guard system that closed the gap; every gate traces to a specific postmortem
• Reduced an entire failure class to an offline-checkable invariant. Verified against the full asset inventory: 81% of migrations required a namespace change, not the name change the tooling assumed
• Owned response for 5 production incidents including 2 SEV3s (one at $5.27M/day) — fix-forward, zero reverts, permission grants coordinated across 6+ owning teams
• Co-built an AI coding skill encoding the full remediation methodology: engineer ramp-up 2 weeks → 2 days, per-asset cycle time down 3–5×
• Work recognized in a company-wide announcement to the Data organization, with the difficulty of this workstream called out explicitly by senior engineering leadership

• Designed prompt templates with stable cacheable prefixes + minimal variable context — cut input tokens 60% across 800+ AI-assisted diffs
• Routed simple transforms to local template engines (zero API cost); reserved Claude for ambiguous multi-consumer rewrites only — eliminated 30% of unnecessary invocations
• Enforced retrieval-over-stuffing: pass 40-line function blocks, not 2,000-line files. Structured JSON output schemas cap response length to what's needed
• Capped agent retry loops at 3 iterations with pre-flight input validation — killed retry spirals burning 40x normal token budget on stale inputs
• Tracked cost per landed diff (not per API call) — optimized for first-pass success rate, driving effective cost to ~$0.80/remediation at 800+ diff scale

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