Principal Engineer Data Engineering, AI & Distributed Systems

Job Summary

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
  • Distributed Systems Patterns
• Define standards for:
  • Secure coding
  • API design
  • CI/CD
  • Test automation
  • 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