Lead Data Engineer

About This Role

Wells Fargo is seeking a Lead Data Engineer to join the Enterprise Services Technology (EST) Cyber Security organization. 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.

Required Technical Skills

Posting End Date

17 Sep 2026. Job posting may come down early due to volume of applicants.