Principal Engineer - Public Cloud Data

Wells Fargo•Chandler, AZ

2d•$159,000 - $254,000•Hybrid

About The Position

The Principal Engineer for Public Cloud Data, Analytics & Agentic AI Platform is the senior-most technical leader responsible for defining and driving the strategic architecture, engineering standards, and innovation roadmap for enterprise-scale data, analytics, and Agentic AI capabilities on public cloud platforms. This role serves as the technical authority for modern data platforms, AI-native architectures, and autonomous agent solutions that enable self-service analytics, intelligent automation, real-time decisioning, and enterprise-scale data products. The Principal Engineer partners with engineering, architecture, cybersecurity, risk, compliance, product, and business leaders to accelerate cloud transformation while maintaining security, governance, resilience, and regulatory compliance.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
7+ years designing enterprise-scale data and analytics platforms
5+ years delivering AI/ML, Generative AI, or Agentic AI solutions in complex enterprise environments
Proven experience leading enterprise-wide cloud transformation initiatives

Nice To Haves

Deep expertise in one or more public cloud platforms: Google Cloud Platform (preferred), Microsoft Azure, Amazon Web Services
Strong experience with: Data Lakehouse architectures, BigQuery, Snowflake, Databricks, Starburst, or equivalent platforms, Streaming and event-driven architectures, Data governance, metadata, lineage, and catalog solutions, Kubernetes and container platforms, Infrastructure as Code and DevOps automation
AI & Agentic AI Expertise: Experience designing enterprise AI platforms and MLOps frameworks, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI architectures, Multi-agent systems, Vector databases, Model evaluation and monitoring, MCP (Model Context Protocol) ecosystems
Experience implementing production-grade AI governance and controls
Advanced degree in Computer Science, Engineering, Data Science, AI, or related field
Industry certifications in public cloud, data engineering, AI/ML, or security.
Experience in highly regulated industries such as financial services, healthcare, or insurance
Contributions to industry standards, patents, open-source projects, or published technical thought leadership

Responsibilities

Define the target-state architecture for cloud-native data, analytics, AI/ML, and Agentic AI platforms
Establish engineering standards, reference architectures, reusable patterns, and technical governance across the enterprise
Drive modernization of legacy data platforms into scalable cloud-native ecosystems
Lead technology evaluations and strategic adoption of emerging public cloud capabilities
Architect and scale enterprise lakehouse, data mesh, streaming, and real-time analytics platforms
Define standards for data ingestion, transformation, governance, metadata management, lineage, and data quality
Enable self-service data products and analytics capabilities across business lines
Lead the development of Agentic AI solutions that automate engineering, operations, governance, and analytics workflows
Define enterprise frameworks for AI agents, orchestration, reasoning engines, MCP-based integrations, and human-in-the-loop controls
Architect AI-enabled automation for: Data onboarding, Pipeline generation, Metadata enrichment, Policy enforcement, Data quality validation, Incident remediation, Operational intelligence
Establish standards for responsible AI, explainability, model governance, and auditability
Provide technical leadership across public cloud services including compute, storage, networking, security, AI, analytics, and DevOps ecosystems
Drive platform reliability, scalability, resiliency, observability, disaster recovery, and operational excellence
Partner with engineering teams to implement Infrastructure as Code (IaC), platform automation, and self-service capabilities
Establish SLOs, reliability metrics, and engineering KPIs
Partner with Cyber Security, IAM, Risk, Audit, and Regulatory Compliance teams
Ensure cloud platforms meet enterprise security, governance, privacy, and regulatory standards
Architect secure-by-design and compliance-by-design platform capabilities
Drive implementation of AI governance controls for enterprise-scale Agentic AI adoption
Design high-performance, secure, and cost-efficient cloud data ecosystems supporting regulatory and business requirements