Principal Engineer - Public Cloud Data

Wells Fargo•Chandler, AZ

3d•$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 strategic architecture, engineering standards, and the innovation roadmap for enterprise-scale data, analytics, and Agentic AI capabilities on public cloud platforms. The role provides 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. It partners with engineering, architecture, cybersecurity, risk, compliance, product, and business leaders to advance cloud transformation while maintaining security, governance, resilience, and regulatory compliance. The role defines target-state architecture and enterprise engineering standards; modernizes legacy data platforms; and architects scalable lakehouse, data mesh, streaming, and real-time analytics platforms. It enables self-service data products, develops Agentic AI solutions and frameworks, and establishes responsible AI, model governance, explainability, and auditability standards. It also leads technology evaluation and adoption across public cloud ecosystems, and drives platform reliability, security, automation, cost efficiency, and operational excellence. The position partners with cybersecurity, IAM, risk, audit, and regulatory compliance teams to ensure secure-by-design and compliance-by-design platform capabilities. This position offers a hybrid work schedule; telecommuting is not an option. It is not eligible for visa sponsorship, and relocation assistance is not available. The listed base pay range is $159,000 to $254,000; employees may also be eligible for incentive opportunities. Wells Fargo describes a comprehensive benefits package for eligible employees.

Requirements

At least 7 years of engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
At least 7 years designing enterprise-scale data and analytics platforms.
At least 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, or Amazon Web Services.
Strong experience with data lakehouse architectures.
Experience with BigQuery, Snowflake, Databricks, Starburst, or equivalent platforms.
Experience with streaming and event-driven architectures.
Experience with data governance, metadata, lineage, and catalog solutions.
Experience with Kubernetes and container platforms.
Experience with Infrastructure as Code and DevOps automation.
Experience designing enterprise AI platforms and MLOps frameworks.
Experience with Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI architectures, multi-agent systems, vector databases, model evaluation and monitoring, and MCP ecosystems.
Experience implementing production-grade AI governance and controls.
Advanced degree in Computer Science, Engineering, Data Science, AI, or a 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 target-state architecture for cloud-native data, analytics, AI/ML, and Agentic AI platforms.
Establish enterprise engineering standards, reference architectures, reusable patterns, and technical governance.
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 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, and operational intelligence.
Establish standards for responsible AI, explainability, model governance, and auditability.
Provide technical leadership across public cloud 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, 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.
Fulfill applicable risk program, policy, compliance, escalation, remediation, and risk-decision responsibilities described in the posting.