Principal Engineer - Data Engineering Architect

About This Role
Wells Fargo is seeking a Principal Engineer / Architect ( Data Engineering )to define and advance the data information strategy, information models, data sourcing standards, and data enablement roadmap across Enterprise Risk, Audit, and Finance Technology.
This is a horizontal architecture role supporting multiple business and technology domains, including Financial Risk, Non-Financial Risk, Finance, Treasury, Regulatory Reporting, Internal Audit, Independent Testing, Model Risk, and Financial Crimes.
The role will shape how enterprise information is identified, defined, sourced, modeled, governed, integrated, and made consumable across ERAFT. The role will establish common information models, semantic standards, authoritative-source guidance, reusable data products, and architecture patterns that enable consistent reporting, analytics, regulatory traceability, and artificial intelligence use cases.
The ideal candidate brings a strong combination of information architecture, enterprise data architecture, financial-services and banking domain knowledge, data governance, sourcing strategy, semantic modeling, integration design, analytics enablement, and stakeholder leadership. This individual must be able to work independently, influence decisions across business and technology teams, and translate complex business, regulatory, and technology requirements into practical and scalable data architecture.
In This Role, You Will
Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups
Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking
Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions
Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions
Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization
Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership.
Required Qualifications:
7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
7+ years of experience in data architecture, information architecture, enterprise architecture, information modeling, or data-platform design.
7+ years of experience supporting banking, financial services, finance, risk, audit, regulatory reporting, compliance, or control-related domains.
Desired Qualifications:
Data Information Strategy and Architecture
Define and own the data information strategy and target-state information architecture across ERAFT.
Develop current-state, target-state, and transition-state architectures covering information domains, data sourcing, information models, metadata, lineage, data products, semantic access, integration, and consumption.
Define canonical information models and common business concepts that can be reused across Risk, Finance, Audit, regulatory reporting, analytics, and AI use cases.
Establish standards and artifacts for conceptual and logical data models, taxonomies, ontologies, business glossaries, source-to-target mappings, data flows, data lineage views, and semantic relationships.
Align ERAFT information architecture with enterprise data, metadata, privacy, security, retention, records-management, and regulatory standards.
Define architecture principles that enable common information to be understood and reused while allowing justified domain-specific extensions.
Produce architecture specifications, decision records, reference models, roadmaps, standards, and executive-level recommendations.
Develop POC’s and demos to supplement architecture materials.
Data Sourcing Strategy
Define reusable data sourcing strategies across ERAFT business and technology domains.
Identify authoritative systems of record, systems of reference, authoritative distribution sources, and trusted consumption sources for critical enterprise information.
Establish criteria for selecting the appropriate sourcing approach, including direct-source integration, authoritative data products, APIs, event streams, batch feeds, data warehouses, data lakes, data virtualization, federated queries, and metadata-driven ingestion.
Define sourcing patterns for structured, semi-structured, and unstructured information.
Partner with source-system owners, data owners, and data stewards to assess data availability, ownership, quality, lineage, refresh frequency, historical coverage, access restrictions, and regulatory obligations.
Determine whether information should be physically ingested, virtually accessed, indexed, summarized, referenced, or exposed through a governed data product.
Reduce duplicate sourcing, unnecessary data movement, point-to-point integration, and inconsistent transformation logic.
Define data contracts that document schema, semantics, quality expectations, service levels, usage constraints, and change-management responsibilities between producers and consumers.
Information Modeling and Semantic Alignment
Develop and maintain enterprise and domain-level information models across ERAFT.
Standardize business concepts, definitions, relationships, taxonomies, and metrics across Risk, Finance, Audit, and related regulatory domains.
Define semantic relationships among customers, accounts, products, transactions, legal entities, positions, risks, controls, models, obligations, issues, findings, evidence, business processes, reports, and organizational structures.
Partner with business data owners and governance teams to approve common definitions and resolve semantic conflicts.
Support subject-area taxonomy alignment, conceptual modeling, glossary analysis, ontology evaluation, and capability mapping.
Define semantic-layer patterns that separate business meaning from physical data structures and technology implementations.
Evaluate ontology and knowledge-graph approaches where they improve cross-domain discovery, traceability, impact analysis, aggregation, or AI-enabled access.
Promote reuse of common information concepts while preserving appropriate business context for individual domains.
Data Governance, Quality, Metadata, and Lineage
Define information-governance requirements for ERAFT data products, platforms, and solutions.
Partner with data owners and stewards to establish accountability for business terms, data domains, data products, and Critical Data Elements.
Define data-quality expectations for completeness, accuracy, timeliness, validity, consistency, and uniqueness.
Establish pattern