Data Engineering Senior Manager

Wells Fargo & Company•Charlotte, NC

1d•$139,000 - $217,000•Hybrid

About The Position

Wells Fargo is seeking a strategic and transformational Data Engineering Senior Manager within Home Lending Data & Insights, a data engineering organization within Consumer Technology. The role provides engineering leadership across the Capital Markets and Shared Data Services product areas, supporting loan delivery, investor reporting, reusable data capabilities, enterprise data consumption, analytics, artificial intelligence, and risk management. Shared Data Services provides foundational capabilities such as semantic data models, master and reference data management, data distribution patterns, curation frameworks, cloud services, and operational enablement. Capital Markets supports business functions related to loan delivery and investor reporting. The leader will build and develop high-performing engineering teams, modernize data platforms, advance cloud adoption, enable AI-ready data products, and strengthen engineering standards, governance, resiliency, and operational excellence. The position requires strategic leadership, technical depth, product partnership, and executive influence to deliver business value in a highly regulated financial services environment. The role develops and executes data engineering strategy aligned with enterprise technology, data, and AI priorities; partners with business and technology leaders to identify opportunities; and translates objectives into technology investments and roadmaps. It leads AI-ready data platform development and adoption of modern architectures, working with Data Science, Analytics, Risk, Product, and Architecture teams. It oversees enterprise-scale data platforms, data lakes, streaming solutions, and cloud-native architectures, while ensuring solutions meet enterprise standards and supporting risk management, security, governance, privacy, regulatory compliance, audits, and remediation.

Requirements

7+ years of Data Engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination.
3+ years of management or leadership experience.
5+ years leading large-scale data engineering initiatives and enterprise data platforms.
4+ years delivering cloud-based data and analytics solutions.
5+ years of experience in data architecture, data modeling, ETL/ELT, metadata management, data governance, and distributed data processing.
3+ years influencing and collaborating with senior technology and business leaders.

Nice To Haves

Experience supporting enterprise AI, Machine Learning, Generative AI, or advanced analytics initiatives.
Experience with modern cloud data platforms such as Databricks, Snowflake, Azure, AWS, or GCP.
Experience with big data and streaming technologies including Spark, Kafka, and related ecosystems.
Strong understanding of AI governance, responsible AI principles, and AI model operationalization.
Proven ability to lead organizational change and technology transformation initiatives.
Experience operating in highly regulated financial services environments.
Exceptional executive communication, stakeholder management, and strategic planning capabilities.
Advanced degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.

Responsibilities

Develop and execute a multi-year data engineering strategy aligned with enterprise technology, data, and AI priorities.
Partner with senior business and technology leaders to identify opportunities for data and AI to improve customer experiences, operational efficiency, risk management, and business outcomes.
Translate business objectives into scalable technology investments and engineering roadmaps.
Drive innovation while balancing operational stability, risk management, and regulatory requirements; promote data-driven decision-making.
Lead development of AI-ready data platforms for machine learning, Generative AI, advanced analytics, and intelligent automation.
Partner with Data Science, Analytics, Risk, Product, and Architecture teams to accelerate AI adoption and operationalize enterprise-scale AI solutions.
Establish practices for data accessibility, model readiness, data quality, lineage, governance, and responsible AI.
Evaluate emerging technologies and use AI and automation to improve engineering productivity and business outcomes.
Drive modern data architectures for real-time analytics, predictive insights, and AI-driven decision-making.
Lead, mentor, and develop data engineers, engineering managers, and technical leaders; foster an inclusive culture of accountability, innovation, collaboration, and continuous learning.
Build organizational capability through coaching, succession planning, leadership development, and talent attraction and retention.
Promote engineering excellence through modern software engineering practices, automation, DevOps, and Agile methodologies.
Oversee the design, development, and support of enterprise-scale data platforms, data lakes, streaming solutions, and cloud-native data architectures.
Ensure solutions are scalable, reliable, secure, and compliant with enterprise standards.
Drive platform modernization using cloud technologies, automation, and reusable engineering patterns.
Establish standards for data quality, observability, metadata management, resiliency, and operational excellence.
Partner with architecture and security teams to align with enterprise technology standards.
Ensure compliance with risk management frameworks, information security policies, and regulatory requirements.
Promote data governance, controls, lineage, privacy, and stewardship practices.
Lead engineering teams through audits, regulatory reviews, and remediation efforts as required.
Balance innovation with sound operational risk management and governance principles.
Fulfill applicable risk and compliance obligations, monitor and escalate issues, and make risk decisions consistent with requirements and business risk appetite.