Enterprise Architect (Data Governance) - Indianapolis, US

An Enterprise Architect I — Data & Data Governance is responsible for designing, governing, and evolving the enterprise data architecture and data-governance program. This role applies TOGAF, ArchiMate, and DAMA-DMBOK to translate business strategy into target-state data architectures, canonical data models, governance standards, and master- and reference-data capabilities. The Data Enterprise Architect partners with business stakeholders, data stewards, security, privacy, compliance, and delivery teams to define data domains, data-quality and metadata programs, and data-platform patterns that improve data trust, accessibility, and regulatory compliance. They are accountable for ensuring data solutions align with enterprise principles, regulatory obligations (PII, PCI, state insurance regulations), and the organization’s long-term technology vision. Primary Responsibilities •Maintains the holistic view of the organization’s data strategy, data domains, master and reference data, data products, metadata, lineage, and information assets across operational and analytical estates. •Develops and maintains target-state data reference architectures and architecture-vision artifacts using TOGAF ADM (especially Phase C — Information Systems / Data Architecture) and ArchiMate viewpoints (motivation, business information, data dissemination, data realization, application cooperation). •Transition architectures, and data roadmaps; partners with data owners, stewards, product, and delivery teams to sequence data modernization and governance maturity. •Defines and governs the enterprise data architecture — conceptual, logical, and physical data models; canonical structures; data-domain definitions; master and reference data; and the system-of-record / system-of-reference landscape across operational, analytical, and graph platforms. •Establishes data architecture and governance standards aligned to the DAMA-DMBOK knowledge areas: data governance, data architecture, data modeling & design, data storage & operations, data security, data integration & interoperability, reference & master data, data warehousing & BI, metadata, and data quality. May 20, 2026 Version: 2 •Drives a common approach to data tooling and platform selection — catalogs, MDM, lineage, data quality, and identity-resolution capabilities — and contributes to (or chairs) the Data Governance Council and Architecture Review Board for data-impacting initiatives. •Authors reference patterns for data ingestion, transformation, storage (warehouse, lake, lakehouse, graph, operational data store), and serving (API and data products, analytical marts); reviews solution designs for alignment with patterns and standards. •Partners with Information Security to embed data classification, encryption, tokenization, masking, fine-grained access control, retention, and lineage into data standards — addressing PII, PCI, and state insurance regulatory requirements. •Provides stewardship and quality perspective on architectural choices: data ownership and stewardship models, data-quality dimensions and SLAs, metadata maturity, and the operating model for data domains and data products. •Researches and evaluates emerging data technologies — data catalogs, MDM platforms, data mesh / data fabric approaches, lakehouse formats, graph and vector stores, identity-resolution tooling — and advises on adoption, risk, and roadmap implications. •Operates across organizational and computing “silos” — bridges mainframe data stores, operational systems, SaaS data, and analytical platforms by defining data-integration, master-data, and identity-resolution patterns that span the enterprise.