{"id":3484937,"url":"https://alion.io/job/wells-fargo-principal-engineer-data-engineering-architect","title":"Principal Engineer - Data Engineering Architect","company":{"id":1977,"name":"Wells Fargo","domain":"wellsfargo.com","url":"https://alion.io/company/wells-fargo","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":10,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":3,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":40000,"max_usd":86000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Neo4j","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-28T00:00:00Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T07:30:14Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About This Role\nWells 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.\nThis 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.\nThe 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.\nThe 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.\nIn This Role, You Will\nAct 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\nLead 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\nTranslate 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\nProvide vision, direction and expertise to leadership on implementing innovative and significant business solutions\nMaintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization\nStrategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership.\nRequired Qualifications:\n7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education\n7+ years of experience in data architecture, information architecture, enterprise architecture, information modeling, or data-platform design. \n7+ years of experience supporting banking, financial services, finance, risk, audit, regulatory reporting, compliance, or control-related domains. \nStrong Experience in APIs, Kafka or event streaming, ETL and ELT, data virtualization, data lakes, lakehouse platforms, and enterprise data warehouses. \nExperience with technologies such as Dremio, Databricks, Snowflake, Spark, Python, SQL, Neo4J, Protégé, object storage, and metadata or catalog platforms. \nDesired Qualifications:\nData Information Strategy and Architecture\nDefine and own the data information strategy and target-state information architecture across ERAFT. \nDevelop current-state, target-state, and transition-state architectures covering information domains, data sourcing, information models, metadata, lineage, data products, semantic access, integration, and consumption. \nDefine canonical information models and common business concepts that can be reused across Risk, Finance, Audit, regulatory reporting, analytics, and AI use cases. \nEstablish 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. \nAlign ERAFT information architecture with enterprise data, metadata, privacy, security, retention, records-management, and regulatory standards. \nDefine architecture principles that enable common information to be understood and reused while allowing justified domain-specific extensions. \nProduce architecture specifications, decision records, reference models, roadmaps, standards, and executive-level recommendations. \nDevelop POC’s and demos to supplement architecture materials. \nData Sourcing Strategy\nDefine reusable data sourcing strategies across ERAFT business and technology domains. \nIdentify authoritative systems of record, systems of reference, authoritative distribution sources, and trusted consumption sources for critical enterprise information. \nEstablish 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. \nDefine sourcing patterns for structured, semi-structured, and unstructured information. \nPartner 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. \nDetermine whether information should be physically ingested, virtually accessed, indexed, summarized, referenced, or exposed through a governed data product. \nReduce duplicate sourcing, unnecessary data movement, point-to-point integration, and inconsistent transformation logic. \nDefine data contracts that document schema, semantics, quality expectations, service levels, usage constraints, and change-management responsibilities between producers and consumers. \nInformation Modeling and Semantic Alignment\nDevelop and maintain enterprise and domain-level information models across ERAFT. \nStandardize business concepts, definitions, relationships, taxonomies, and metrics across Risk, Finance, Audit, and related regulatory domains. \nDefine semantic relationships among customers, accounts, products, transactions, legal entities, positions, risks, controls, models, obligations, issues, findings, evidence, business processes, reports, and organizational structures. \nPartner with business data owners and governance teams to approve common definitions and resolve semantic conflicts. \nSupport subject-area taxonomy alignment, conceptual modeling, glossary analysis, ontology evaluation, and capability mapping. \nDefine semantic-layer patterns that separate business meaning from physical data structures and technology implementations. \nEvaluate ontology and knowledge-graph approaches where they improve cross-domain discovery, traceability, impact analysis, aggregation, or AI-enabled access. \nPromote reuse of common information concepts while preserving appropriate business context for individual domains. \nData Governance, Quality, Metadata, and Lineage\nDefine information-governance requirements for ERAFT data products, platforms, and solutions. \nPartner with data owners and stewards to establish accountability for business terms, data domains, data products, and Critical Data Elements. \nDefine data-quality expectations for completeness, accuracy, timeliness, validity, consistency, and uniqueness. \nEstablish patterns for data controls, reconciliation, validation, observability, and issue remediation. \nDefine end-to-end lineage requirements from authoritative sources through ingestion, transformation, semantic models, reports, analytics, models, AI solutions, and downstream consumers. \nEstablish metadata requirements that support discovery, reuse, impact analysis, auditability, explain ability, and regulatory response. \nEnsure that information architecture addresses privacy, security, entitlements, data classification, retention, records management, and permitted use. \nPromote common metadata, glossary, domain, ownership, and quality standards across on-premises, cloud, and hybrid environments. \nData Enablement, Analytics, and AI\nDefine the data foundation required to support dashboards, reports, analytics, advanced monitoring, and AI-enabled insights. \nPartner with analytics and business-intelligence teams to define fit-for-purpose data products, semantic layers, data marts, dimensions, metrics, and reporting definitions. \nEnsure that business metrics and analytical concepts are consistently defined, traceable to authoritative information, and reusable across domains. \nDefine information structures and metadata required for AI retrieval, grounding, context management, model inputs, evaluation, and human review. \nEstablish data-readiness criteria for AI use cases, including quality, lineage, authority, access, consent, context, explainability, and traceability. \nEnable governed AI access to business glossaries, metadata, lineage, quality indicators, relationships, and approved enterprise knowledge. \nEnsure that AI-enabled consumption is explainable, auditable, secured, and aligned with applicable governance expectations. \nPromote an information-first approach in which AI solutions consume governed enterprise context rather than creating isolated copies and conflicting definitions. \nIntegration and Data-Platform Architecture\nPartner with enterprise architects, application architects, platform architects, solution architects, and engineering teams to translate information architecture into implementable solutions. \nDefine patterns for APIs, event streaming, batch ingestion, ETL and ELT, files, data virtualization, federated queries, and metadata-driven connectors. \nEnsure that platform designs support scalability, resiliency, performance, security, maintainability, and lifecycle management. \nAlign information architecture with enterprise cloud, hybrid, on-premises, analytics, and integration standards. \nProvide data-architecture input to platform selection, vendor assessment, and build-versus-buy decisions. \nGuide modernization from fragmented legacy data flows to governed, reusable, and interoperable enterprise capabilities. \nMaintain a clear separation between business information architecture, logical data architecture, and physical technology implementation. \nStakeholder Management and Architecture Governance\nAct as a trusted information-architecture adviser to business, data, technology, architecture, risk, finance, audit, compliance, and control leaders. \nTranslate business and regulatory requirements into practical information and data architecture decisions. \nFacilitate decisions involving information ownership, authoritative sourcing, semantic definitions, quality expectations, data sharing, and consumption patterns. \nPresent data and information architecture strategies, target states, trade-offs, risks, and roadmap recommendations to senior leaders and governance forums. \nLead and contribute to architecture reviews, design reviews, data-governance forums, and platform decision-making bodies. \nIndependently drive architecture decisions while balancing risk, cost, regulatory requirements, delivery commitments, and long-term sustainability. \nDocument major sourcing, modeling, semantic, integration, and platform decisions through architecture decision records. \nIdentify architecture risks, dependencies, gaps, exceptions, and remediation requirements. \nMentor architects, data modelers, analysts, and engineers in information-architecture practices. \nJob Expectations:\nThe Principal Engineer - Lead Architect will be expected to:\nExperience spanning multiple Risk, Finance, Audit, Treasury, Financial Crimes, Model Risk, regulatory, or enterprise data-management domains. \nExperience with metadata management, data 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