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