Enterprise Architect I — Data & Data Governance

Job Purpose/Summary An Enterprise Architect I — Data & Data Governance is responsible for designing, governing, and evolving Indiana Farm Bureau Insurance’s 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. • 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. Minimum Knowledge and Requirements • Minimum of an associate’s degree in a related field; bachelor’s degree in Computer Science, Information Systems, Data Science, or related discipline preferred. • Minimum of 3–5 years of relevant enterprise- or solution-architecture experience, with at least 2 years focused on data architecture, data modeling, or data governance. • TOGAF experience required — demonstrated ability to apply the ADM (Preliminary through Phase H, with depth in Phase C — Data Architecture), content metamodel, and architecture repository on real engagements. TOGAF certification preferred. • ArchiMate modeling experience required — fluent in motivation, strategy, business, application, and technology layers, with strong command of the data-relevant elements and viewpoints (business information, data dissemination, data realization, application cooperation). ArchiMate 3 certification preferred. • DAMA-DMBOK experience preferred — working command of the DMBOK knowledge areas with practical application in at least four (e.g., data governance, data architecture, data modeling, metadata, data quality, master and reference data). CDMP (Certified Data Management Professional) certification preferred (Associate, Practitioner, or Master level). • Strong data modeling skill required — conceptual, logical, and physical modeling across normalized and dimensional paradigms, with familiarity in modern patterns including Data Vault 2.0 and data-mesh / data-product approaches. Hands-on with at least one modeling tool (Erwin, ER/Studio, PowerDesigner, or equivalent). • Working knowledge of enterprise data platforms — cloud data platforms (Databricks Lakehouse, Snowflake, AWS Redshift), graph databases (AWS Neptune or equivalent), operational data stores, master-data-management platforms, and data catalogs (Collibra, Alation, Atlan, Microsoft Purview, AWS Glue Data Catalog, or equivalent). Familiarity with data-ingestion tools (Fivetran, Kafka, AWS Glue, or equivalent) and data-quality tooling. • Knowledge of identity-resolution and master-data patterns — deterministic and probabilistic matching, survivorship, identity graphs, and the trade-offs between centralized MDM and federated / domain-aligned approaches. • Privacy, security, and regulatory fluency — working knowledge of PII / PCI handling, GDPR and CCPA principles, and insurance regulatory considerations (NAIC Model Audit Rule, state insurance data and privacy regulations). • Ability to translate complex data and architecture concepts into terms understandable to technical and non-technical management and staff. • Strong communication, presentation, and interpersonal skills to build credible relationships with IT and Business partners and to identify opportunities, challenges, and solutions for the company. • Skilled in preparing and delivering data documentation: ArchiMate views, conceptual and logical data models, entity-relationship diagrams, data-flow and data-lineage diagrams, capability maps, and reference architectures. • Ensure that data solutions are well engineered, trustworthy, observable, secure, and aligned with the enterprise vision. • Research solutions and emerging data technologies on the market for particular technical and business needs. • Strong foundational background in data engineering, data warehousing, or analytics on large or complex systems, including ETL / ELT, streaming, and data-API patterns. • Provide strategic consultation to business and IT teams on data direction, technology selection, governance maturity, and architectural trade-offs.