Lead Data Engineer

About This Role

Wells Fargo is seeking a Lead Data Engineer in Technology as part of Cybersecurity. This role will help drive the evolution of enterprise data platforms supporting cybersecurity, technology risk, governance, analytics, reporting, AI-enabled services, and enterprise workflow capabilities. The ideal candidate will possess strong expertise in enterprise data engineering, large-scale ETL/ELT development, cloud-native technologies, event-driven architectures, and modern data integration patterns. This individual will serve as a technical leader responsible for designing scalable, secure, and resilient data solutions that support critical cybersecurity and enterprise technology functions across Wells Fargo.

In This Role, You Will

• Lead the design and implementation of enterprise-scale data platforms and solutions.
• Design and develop scalable data ingestion, transformation, enrichment, and distribution frameworks.
• Build and optimize high-performance ETL/ELT pipelines supporting operational, analytical, and regulatory workloads.
• Design and maintain enterprise data models, data architecture standards, and integration frameworks.
• Develop and support event-driven data processing solutions using streaming and messaging technologies.
• Create and optimize SQL-based solutions for analytics, reporting, troubleshooting, and operational processing.
• Partner with cybersecurity, technology risk, architecture, engineering, and business teams to deliver strategic data capabilities.
• Support advanced analytics, AI-enabled services, and enterprise reporting solutions through well-architected data services.
• Lead technical design activities, architecture reviews, and engineering best practices.
• Research and resolve complex production issues while ensuring platform stability, scalability, and performance.
• Mentor and provide technical guidance to data engineers throughout the development lifecycle.
• Drive continuous improvement in data engineering standards, automation, and operational excellence.
• Demonstrate proficiency in using AI-assisted development and analysis tools (e.g., GitHub Copilot and approved code-centric agents).
• Leverage AI to accelerate system design, coding, testing, analysis, and troubleshooting.
• Apply strong technical judgment when validating and integrating AI-assisted outputs into solutions.
• Understand and account for model limitations, security risks, and operational considerations.
• Apply AI responsibly in development and production environments.
• Ensure AI usage aligns with security, compliance, privacy, and ethical standards.

Required Qualifications

• 5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• Strong expertise in data modeling, data architecture, and enterprise data integration patterns.
• Strong experience developing and optimizing complex SQL queries and database solutions.
• Experience building scalable ETL/ELT pipelines and data ingestion frameworks.
• Experience integrating with ServiceNow and enterprise workflow platforms.

Desired Qualifications

• Experience supporting Cyber Security, Technology Risk, Governance, Risk & Compliance (GRC), or Enterprise Technology initiatives.
• Experience designing and supporting cybersecurity, asset inventory, operational reporting, or technology risk data solutions.
• Experience building enterprise reporting, analytics, and data consumption solutions.
• Experience deploying and supporting solutions on OpenShift (OCP), Kubernetes, or similar container platforms.
• Experience implementing data governance, metadata management, data lineage, and audit capabilities.
• Experience working with NoSQL databases, graph databases, Redis, or distributed caching technologies.
• Experience managing EPLX deployment pipelines and release management processes.
• Experience implementing database schema and change management through Liquibase.
• Familiarity with Apache Iceberg data platforms and modern lakehouse architectures.
• Experience developing or consuming PySpark data contracts and enterprise data products.
• Experience supporting AI-enabled data services, Generative AI, Retrieval-Augmented Generation (RAG), or machine learning platforms.
• Experience implementing enterprise data quality and master data management solutions.
• Experience working within highly regulated financial services environments.
• Experience leading large-scale modernization, cloud migration, or platform transformation initiatives.
• Experience supporting large-scale data warehouse environments.
• Experience designing and implementing event-driven architectures using Kafka or similar streaming technologies.
• Experience developing and supporting enterprise API integrations and data services.
• Experience working with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
• Experience working with Oracle databases and enterprise-scale data ecosystems.
• Demonstrated ability to solve complex data engineering challenges involving multiple systems and data domains.
• Strong collaboration skills with cross-functional engineering, architecture, cybersecurity, technology risk, and business teams.
• Ability to provide technical leadership, mentoring, and guidance to engineering teams.

Posting End Date: 17 Sep 2026. Job posting may come down early due to volume of applicants. Reference Number: R-572832-1.