Principal Data Architect, Chief Data Office

Wells Fargo

Confirmed live yesterday High trust
Closes in 2 days Hybrid

Quick summary

Work type
Hybrid
Location
Charlotte, NCIrving, TXWest Des Moines, IAMinneapolis, MN
Salary
$159,000–$279,000 / yr
Employment
Full-time
Posted
5 days ago
Freshness
Confirmed live yesterday
Closes
Sep 30, 2026 (soon)

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $203k
This role $219k
$141k most similar roles pay here $294k

This role pays more than 68% of similar roles. Most pay $173,200–$232,265 — the shaded band above. At the midpoint, this role pays about $219k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Wells Fargo

Wells Fargo & Company is one of the largest banks in the United States, providing banking, investment, mortgage, and consumer and commercial finance products and services nationwide. Industry: Banking & Financial Services

Wells Fargo currently has 34 open roles on FindRole.

Listed pay typically runs $159,000–$254,000 across 17 roles with salary data.

Most-posted roles

View all roles at Wells Fargo

At a glance

TL;DR · Principal Data Architect, Chief Data Office

Principal Data Architect Chief Data Office is a hands-on technologist role within the Chief Data Office focused on shaping modern data architecture across cloud and on-premise environments in a large-scale banking ecosystem. The individual will design scalable patterns for application, analytics, workflow, and AI-enabled workloads while evolving legacy systems into secure, resilient platforms. Key responsibilities include developing architectural frameworks, defining strategic tools, and creating reference architectures to solve complex data challenges. The role requires expertise in distributed data systems, relational, NoSQL, and columnar database technologies. Candidates will utilize SQL, Python, Java, Spark, Kafka, Airflow, and various API-based integration patterns. Essential skills include data modeling, security architecture, and designing for AI/ML workloads like vector search and feature engineering. The role addresses the technical challenge of modernizing complex financial data environments while balancing innovation with cost and scalability.

What you'll do

  • Design scalable data architecture patterns for application, analytics, workflow, and AI-enabled workloads across cloud and on-prem environments.
  • Develop technical roadmaps to transition legacy systems into secure, resilient, and future-ready platforms.
  • Provide architectural guidance and proof-of-concept code to solve complex data challenges for senior leadership.
  • Define and implement reference architectures, reusable design patterns, and technical guardrails for enterprise data systems.
  • Manage the selection of strategic tools and ensure applications adhere to corporate standards and security policies.
  • Evaluate non-functional requirements including latency, throughput, scalability, and cost of ownership to optimize ROI.
  • Advise senior leadership on transformation strategies by defining current states, target states, and transition architectures.
  • Identify and recommend innovative technologies to enhance operations and provide a competitive advantage for the organization.

What we're looking for

  • 7+ years of experience in data architecture, data engineering, database platforms, or enterprise technology roles.
  • 7+ years of experience designing enterprise-scale data architectures across hybrid cloud, public cloud, private cloud, and on-premises platforms.
  • 7+ years of experience with relational, NoSQL, columnar, distributed, and shared-nothing database technologies.
  • Experience in large-scale financial services or banking environments is required.
  • Ability to design scalable architectures using partitioning, sharding, replication, workload isolation, horizontal scaling, and distributed processing patterns (preferred).
  • Experience architecting batch, streaming, event-driven, real-time, near-real-time, and API-based data integration patterns (preferred).
  • Hands-on capability with technologies such as SQL, Python, Java, Spark, Kafka, Airflow, APIs, and modern data pipeline frameworks (preferred).
  • Expertise in data modeling including conceptual, logical, physical, dimensional, canonical, domain-driven, and event-based models (preferred).

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