Principal Engineer - Data Engineering Architect

About the job

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.

- Strong Experience in APIs, Kafka or event streaming, ETL and ELT, data virtualization, data lakes, lakehouse platforms, and enterprise data warehouses.

- Experience with technologies such as Dremio, Databricks, Snowflake, Spark, Python, SQL, Neo4J, Protégé, object storage, and metadata or catalog platforms.

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 patterns for data controls, reconciliation, validation, observability, and issue remediation.

- Define end-to-end lineage requirements from authoritative sources through ingestion, transformation, semantic models, reports, analytics, models, AI solutions, and downstream consumers.

- Establish metadata requirements that support discovery, reuse, impact analysis, auditability, explain ability, and regulatory response.

- Ensure that information architecture addresses privacy, security, entitlements, data classification, retention, records management, and permitted use.

- Promote common metadata, glossary, domain, ownership, and quality standards across on-premises, cloud, and hybrid environments.

Data Enablement, Analytics, and AI

- Define the data foundation required to support dashboards, reports, analytics, advanced monitoring, and AI-enabled insights.

- Partner with analytics and business-intelligence teams to define fit-for-purpose data products, semantic layers, data marts, dimensions, metrics, and reporting definitions.

- Ensure that business metrics and analytical concepts are consistently defined, traceable to authoritative information, and reusable across domains.

- Define information structures and metadata required for AI retrieval, grounding, context management, model inputs, evaluation, and human review.

- Establish data-readiness criteria for AI use cases, including quality, lineage, authority, access, consent, context, explainability, and traceability.

- Enable governed AI access to business glossaries, metadata, lineage, quality indicators, relationships, and approved enterprise knowledge.

- Ensure that AI-enabled consumption is explainable, auditable, secured, and aligned with applicable governance expectations.

- Promote an information-first approach in which AI solutions consume governed enterprise context rather than creating isolated copies and conflicting definitions.  Integration and Data-Platform Architecture

- Partner with enterprise architects, application architects, platform architects, solution architects, and engineering teams to translate information architecture into implementable solutions.

- Define patterns for APIs, event streaming, batch ingestion, ETL and ELT, files, data virtualization, federated queries, and metadata-driven connectors.

- Ensure that platform designs support scalability, resiliency, performance, security, maintainability, and lifecycle management.

- Align information architecture with enterprise cloud, hybrid, on-premises, analytics, and integration standards.

- Provide data-architecture input to platform selection, vendor assessment, and build-versus-buy decisions.

- Guide modernization from fragmented legacy data flows to governed, reusable, and interoperable enterprise capabilities.

- Maintain a clear separation between business information architecture, logical data architecture, and physical technology implementation.  Stakeholder Management and Architecture Governance

- Act as a trusted information-architecture adviser to business, data, technology, architecture, risk, finance, audit, compliance, and control leaders.

- Translate business and regulatory requirements into practical information and data architecture decisions.

- Facilitate decisions involving information ownership, authoritative sourcing, semantic definitions, quality expectations, data sharing, and consumption patterns.

- Present data and information architecture strategies, target states, trade-offs, risks, and roadmap recommendations to senior leaders and governance forums.

- Lead and contribute to architecture reviews, design reviews, data-governance forums, and platform decision-making bodies.

- Independently drive architecture decisions while balancing risk, cost, regulatory requirements, delivery commitments, and long-term sustainability.

- Document major sourcing, modeling, semantic, integration, and platform decisions through architecture decision records.

- Identify architecture risks, dependencies, gaps, exceptions, and remediation requirements.

- Mentor architects, data modelers, analysts, and engineers i