Principal Engineer – Data Engineering, AI & Distributed Systems

About this role: Wells Fargo is seeking a highly experienced Principal Engineer to provide technical leadership across enterprise data platforms, distributed systems, AI solutions, and cloud-native application architectures. This role will drive the strategic direction for data engineering, real-time analytics, AI-enabled solutions, and microservices platforms that power critical business capabilities at global scale. 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 Desired Qualifications: The ideal candidate is a recognized technical leader with deep expertise in Data Engineering , Java/Spring Boot Microservices , and Generative AI , capable of influencing architecture decisions, mentoring senior engineers, and shaping long-term technology strategy.This role requires balancing innovation with operational excellence, ensuring platforms are secure, scalable, resilient, cost-efficient, and aligned with business outcomes. 7+ years of software engineering experience with significant leadership responsibilities. 7+ years designing and delivering large-scale data engineering solutions. 7+ years leading cloud-native architectures. 3+ years of hands-on Generative AI implementation experience. Experience building mission-critical platforms supporting finance, treasury, risk, or regulatory functions. Experience training, fine-tuning, and deploying LLMs in enterprise environments. Experience implementing enterprise-wide AI governance and responsible AI frameworks. Experience leading large modernization programs involving legacy-to-cloud migration. Proven track record influencing CIO, CTO, and senior executive stakeholders. Data Engineering Leadership Own the strategic direction and modernization of enterprise data platforms. Responsibilities Design and evolve scalable data architectures including: Batch processing Streaming pipelines Real-time event processing Lakehouse architectures Data Mesh and Domain-Oriented Data Products Lead architectural decisions involving: Apache Spark Kafka Iceberg / Delta Lake Snowflake Databricks Flink Cloud-native data platforms Define standards for: Data quality Data lineage Metadata management Observability Governance Data Security and Compliance Drive modernization initiatives from legacy data platforms toward scalable cloud-native architectures. Software Engineering Leadership Provide technical leadership across enterprise application platforms and distributed systems. Responsibilities Design and govern enterprise software architecture using: Java Spring Boot REST APIs Event-Driven Architectures Kafka Distributed Systems Patterns Define standards for: Secure coding API design CI/CD Test automation Observability Documentation Lead architecture reviews and ensure solutions meet: Scalability targets Availability requirements Security standards Performance SLAs Operability objectives Drive adoption of cloud-native engineering practices and modern software delivery models. AI & Generative AI Leadership Lead enterprise adoption of AI and GenAI technologies to transform business processes and engineering productivity. Responsibilities Architect and deliver enterprise-scale GenAI solutions leveraging: Retrieval-Augmented Generation (RAG) Agentic AI frameworks Multi-Agent Orchestration LLM-powered business applications Design end-to-end RAG pipelines including: Document ingestion Chunking strategies Embedding generation Vector databases Retrieval optimization Context augmentation Response orchestration Define enterprise AI architecture and governance standards covering: Responsible AI Model observability Security Compliance Evaluation frameworks Lead implementation of role-based autonomous agent systems using frameworks such as: LangChain LangGraph CrewAI AutoGen Google ADK Partner with Data Science and ML teams to operationalize AI solutions at scale. Cloud & Platform Engineering Responsibilities Lead cloud strategy and architecture across: Azure GCP Design scalable platform solutions using: Docker Kubernetes Infrastructure as Code Cloud-native services Optimize cloud reliability, scalability, performance, and operational cost. Establish resiliency and disaster recovery standards for mission-critical platforms. Strategic Influence Responsibilities Align engineering roadmaps with enterprise business and technology strategy. Shape long-term architecture direction across data, AI, and application platforms. Evaluate emerging technologies and industry trends including: Generative AI Agentic AI Data Mesh Real-Time Analytics Autonomous Engineering Platforms Influence senior leadership and stakeholders on strategic technology investments. Evaluate build-versus-buy decisions, vendor solutions, and platform partnerships. Cross-Functional Collaboration Responsibilities Partner with: Product Management Architecture Data Science Infrastructure Engineering Security Engineering Platform Engineering Business Stakeholders Drive alignment between business objectives and technical execution. Enable access to trusted, reliable, and governed enterprise data assets. Technical Skills Data Engineering: Spark, PySpark, Kafka, Flink, Snowflake, Databricks, Iceberg, Delta Lake Data Lake/Lakehouse architectures Real-Time Streaming Platforms Data Governance and Lineage Software Engineering Java, Spring Boot, Microservices, REST APIs Event-Driven Architectures Distributed Systems AI / GenAI understanding of LLMs RAG Architectures Vector Databases Prompt Engineering Agentic AI Multi-Agent Orchestration Experience with one or more: LangChain LangGraph Google ADK Programming Python, Java Cloud & DevOps Azure / GCP Docker Kubernetes CI/CD platforms Infrastructure as Code Job Expectations: Strong risk‑aware mindset aligned with Wells Fargo’s culture and values Ability to explain complex technical and data concepts to senior business and risk leaders Proven ability to influence and lead in a large, matrixed organization High standards for engineering discipline, documentation, and operational stability Effective leadership during ambiguity, regulatory focus, or high‑visibility initiatives Be Humble: You're smart yet always interested in learning from others. Work Transparently: You always deal in an honest, direct, and transparent way. Take Ownership: You embrace responsibility and find joy in having the answers. Learn More: You regularly self-educate and improve your skill set. Show Gratitude: You show appreciation and respect to