Columbus, Ohio Metropolitan Area
I am a Data & AI Platform Architect experienced in designing enterprise data platforms, analytics ecosystems, cloud-native systems, and AI-powered products. My work spans the full data-platform lifecycle—from requirements discovery, dimensional modeling, semantic-layer design, and ETL architecture to serverless batch pipelines, data quality, observability, governance, and AI-enabled operational systems. Recent architecture work includes: • Data platforms and pipelines — designing independently deployable serverless batch workloads, watermarked incremental pipelines, idempotent processing patterns, and reusable ingestion capabilities for analytics and observability. • Data quality and observability — building declarative YAML- and SQL-driven quality frameworks supporting single-system assertions, cross-system reconciliation, and sharded comparisons. • AI observability — architecting a governed Observe → Optimize → Change platform combining query telemetry, platform metadata, infrastructure signals, deterministic detection, agent tool contracts, and generative AI. • Conversational AI — directing a multi-agent customer copilot using managed conversational orchestration, foundation models, retrieval-augmented generation, secure integration services, and tenant-aware authentication. • Analytics and reporting architecture — contributed to the migration of analytics workloads to a distributed SQL platform, developing dbt-managed aggregation views and reusable business logic that improved reporting performance and consistency. My architecture approach is pragmatic: use managed services when they reduce operational ownership, preserve deterministic controls in critical data paths, define explicit trust and data-integrity boundaries, and document trade-offs rather than defaulting to unnecessary complexity. Earlier in my career, I worked directly with enterprise clients including DIRECTV, Options Clearing Corporation, MetLife, XL Insurance, Daimler Truck, and State Farm. My work included requirements discovery, Kimball dimensional modeling, data-mart design, source-to-target mappings, ETL development, data-quality controls, technical specifications, and coordination across business and engineering teams. Core areas: GCP, data platform architecture, serverless computing, batch data pipelines, semantic layers, dimensional modeling, data quality, observability, data governance, generative AI, conversational AI, and agentic systems.
Architect and hands-on engineer for enterprise data, observability, analytics, and AI platforms on Google Cloud. • Architected an AI observability platform integrating query telemetry, live platform metadata, infrastructure signals, optimization guidance, and change workflows through a governed Observe → Optimize → Change model. • Designed separate Cloud Run–based serverless runtimes for interactive investigations and scheduled monitoring, keeping LLMs outside the critical detection path and reserving generative AI for optimization guidance and structured ticket synthesis. • Architected a declarative YAML- and SQL-driven data-quality framework supporting single-system assertions, cross-system reconciliation, and sharded comparisons without requiring bespoke deployments for each check. • Directed architecture for an authenticated multi-agent customer copilot using Dialogflow CX, Vertex AI, Cloud Run, secure tool integrations, and hybrid retrieval across support, analytics, recommendations, and escalation workflows. • Set technical direction across data and AI platform initiatives, including semantic-layer design, reusable pipeline patterns, agent tool contracts, authentication boundaries, production guardrails, and architecture decision documentation.
Customer facing data consultant delivering enterprise warehouse, data-quality, ETL, and reporting solutions across telecommunications, financial services, and insurance clients. • For DIRECTV and XL Insurance, delivered end-to-end Kimball data warehouse and data-mart solutions spanning requirements, dimensional design, source-to-target mappings, Informatica and DataStage development, reconciliation, scheduling, deployment, and production support. • For Options Clearing Corporation, helped design a repository-driven data-quality framework separating reusable rule definitions from execution results, enabling governed validation and extensible onboarding of checks. • For MetLife, translated business requirements into technical designs, transformation rules, and source-to-target specifications used by junior engineers to implement ETL pipelines. • Served as the technical bridge across client business teams, architects, QA, reporting teams, and distributed engineering groups, coordinating design reviews, UAT findings, releases, and production handoffs. • Applied Kimball modeling, ETL restartability, reconciliation, lineage, and production-control practices using Informatica, IBM DataStage, SQL, Unix, Control-M, and Cognos.
Client: Daimler Truck • Designed and supported Informatica, SQL, Unix, and scheduled batch workflows for supply-chain and logistics reporting, integrating operational data into dimensional warehouse and data-mart structures. • Worked across requirements, source-to-target mapping, ETL development, testing, reconciliation, release coordination, production support, and source-to-report defect investigation.
Client: State Farm Insurance • Developed enterprise ETL pipelines and Kimball-style data-mart components for HR and operational reporting using Informatica PowerCenter, Informatica Data Quality, SQL, Unix, and Control-M. • Worked across requirements analysis, source profiling, technical design, ETL development, testing, deployment, reconciliation, and production support, including slowly changing dimensions, incremental loads, audit controls, reject handling, and restartable batch patterns.