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.