Technical Director – Process Intelligence Data Platform

About the Role

Wells Fargo is seeking a Technical Director to lead the development and delivery of a centralized, governed data and intelligence platform that unifies operational, behavioral, and procedural data across the bank into a single, analytics- and AI-ready ecosystem. This platform is the enterprise foundation for process visibility, optimization, risk management, and AI enablement, including the state, orchestration, observability, and governance capabilities required for production AI workflows.

This is a leadership role. You will own the end-to-end technical strategy, architecture, and delivery roadmap for the platform. You will build and lead the engineering teams responsible for data ingestion, storage, knowledge representation, data cataloging, lineage, governed data access via APIs, and reusable agent harness services such as task state, checkpoints, tool traces, and human review events. You will partner with senior technology and business stakeholders to ensure the platform meets enterprise-wide needs for process intelligence, compliance, and AI-powered decision-making.

What You'll Do

• Technical Vision & Roadmap: Define the multi-year technical strategy for the process intelligence data platform, aligning platform capabilities with enterprise priorities in process mining, compliance, AI governance, and operational efficiency

• Data Platform Architecture: Lead the design and build of a scalable, multi-layered data architecture encompassing storage, semantic and relationship modeling, and AI-ready data layers on cloud infrastructure

• Data Lineage & Catalog: Design and deliver enterprise data lineage capabilities that trace data from source systems through transformation to consumption, and build automated data catalog generation to enable discoverability, impact analysis, and regulatory transparency

• Data Serving APIs: Define and deliver governed data serving APIs that provide secure, performant, and well-documented access to curated datasets, knowledge graph queries, and AI-ready features for downstream consumers (analytics, AI/ML models, compliance tools)

• Agent Harness Enablement: Define and deliver reusable platform services for state management, task orchestration, checkpointing, event logging, tool invocation traces, replay, evaluation datasets, and human-in-the-loop controls to support reliable production AI workflows

• Data Domain Integration: Drive the technical integration of diverse operational data domains—including work execution data, process and procedure knowledge, workflow and operational systems, and AI agent observability data—into a unified, interconnected data model

• Team Leadership: Recruit, develop, and lead a team of data engineers, knowledge graph engineers, ML engineers, and platform engineers; establish engineering standards, delivery practices, and a culture of technical excellence

• Stakeholder Partnership: Collaborate with Process Intelligence leadership, Risk, Compliance, Operations, and AI Governance stakeholders to define data requirements, access models, and platform SLAs

• Data Governance & Trust: Establish data governance frameworks including curated ingestion, validation, end-to-end lineage tracking, access controls, and data quality monitoring to ensure the platform is a trusted, enterprise-approved data source

• Cloud Engineering: Drive cloud-native architecture decisions on GCP and ensure cost-effective, scalable, and secure platform operations

• AI Enablement: Ensure the platform provides high-quality, contextualized data that supports knowledge discovery, semantic search, entity extraction, process mining, and GenAI applications

Required Qualifications:

• 10+ years of Technology Strategic Leadership experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• 4+ years of management or leadership experience

• 3+ years of hands-on architecture or leadership experience with cloud data platforms (GCP preferred)

​Desired Qualifications

• Experience designing platform services for AI workflow orchestration, state management, observability, evaluation, or controlled human review

• Experience with vector databases, embedding pipelines, and AI-ready data infrastructure

• Familiarity with AI agent observability, AI governance, and responsible AI frameworks

• Demonstrated experience building or leading the build of knowledge graphs, graph databases, or semantic data layers at enterprise scale

• Experience designing entity extraction