Felix J. Asencio

Felix Asencio's career in enterprise data began at a Big Four professional services firm, where he worked within the HR Shared Services technology group, building reporting systems and managing the data infrastructure that supported global human capital operations. It was foundational work: the kind that teaches you what governed data flow actually requires when accuracy is non-negotiable and the downstream consumer is a business process, not a dashboard.

From there, the next decade was defined by dimensional modeling. Across a Fortune 500 financial services institution, a national customer loyalty platform, a retail furniture manufacturer, and a multi-client consulting practice, he built data warehouses from requirements to production using the Kimball methodology. Bus matrices, conformed dimensions, change data capture, REST API and flat-file ingestion, on-premises to Azure migrations. The discipline was always the same: design for trust first, then build for scale. He became a practitioner of the methodology in the strict sense, completing formal Kimball DecisionWorks training and applying it across more than a dozen production warehouse builds.

In Director-level and senior architect roles, that hands-on foundation expanded into program leadership. He led cross-environment Power BI deployments, mentored data teams, architected Azure Synapse and Azure Data Factory pipelines spanning legacy ERP systems and modern cloud sources, and built data integration programs in HIPAA-regulated healthcare. At one point he ran a greenfield Azure data platform that required migrating an entire analytics estate from GCP BigQuery to Azure Synapse while standing up Terraform-driven infrastructure and CI/CD pipelines from scratch.

At a logistics enterprise the pattern became unmistakable: the same governance failures he had solved for data warehouses were reappearing in AI-assisted development. He became the organization's first internal champion for AI-assisted development, securing tooling, establishing prompting standards and governance guardrails, and driving team-wide adoption across the data engineering practice. He evaluated third-party AI governance and orchestration platforms on behalf of the CIO, architected a Power BI Copilot deployment with capacity planning and cost modeling, and built Python-based AI ingestion pipelines to feed an enterprise metadata platform spanning legacy and cloud sources. The governance problems he encountered were structurally identical to what he had solved in data warehousing: lineage, trust, requirements fidelity, and alignment to business intent. The bridge to AI governance was not a career change. It was the same discipline applied to a new substrate.

That continuity is the thesis behind Encephalon. As Co-Founder and CTO, he brings the architectural rigor of data warehousing to the governance of autonomous agent systems. Enterprises that struggled to scale governed data pipelines face the same failure modes when they attempt to scale AI agents: context drift, unverifiable outputs, and gaps between business intent and system behavior. Encephalon's platform and services exist to close that gap using the same requirements discipline, lineage thinking, and trust-by-design patterns that made enterprise data warehousing work.