Lead Quantitative & Data Analytics Specialist

Wells Fargo Bank•Charlotte, NC

1d•Hybrid

About The Position

Wells Fargo is seeking an experienced Lead Quantitative and Data Analytics Specialist to lead the design, development, and modernization of data platforms supporting Enterprise Finance Analytical Reporting within our Enterprise Functions Technology (EFT) group. This role is responsible for providing technical leadership across data engineering initiatives, developing scalable data pipelines, enabling analytical reporting, and creating AI-ready data architectures. The successful candidate will lead a high-performing engineering team and collaborate closely with business stakeholders, architects, data scientists, AI engineers, and product owners. The role requires deep technical expertise in data engineering, cloud-native architectures, software engineering best practices, and AI-enabled development frameworks. As the organization expands its adoption of Generative AI and Agentic AI capabilities, this role will also be responsible for building data foundations that support semantic search, Retrieval-Augmented Generation (RAG), AI agents, and enterprise knowledge management solutions.

Requirements

5+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of experience building and supporting large-scale data pipelines, data integration frameworks, and data platforms
5+ years of solution engineering experience, specifically applying mathematical approaches to technical design
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Nice To Haves

Data Engineering experience or equivalent demonstrated through work experience, training, military service, or education
Experience working in Agile, Scrum, or Kanban environments
Proficiency with CI/CD and DevOps tools, including GitHub and Harness
Experience developing solutions using Python
Experience with Devin, Claude, Cursor, Wisdom AI, Rogo, or Tachyon
Strong hands on knowledge with SQL Server database design, development, and optimization
Knowledge and hands on experience designing and developing enterprise-scale data pipelines and data integration solutions
Experience designing and developing scalable data engineering solutions, including data modeling, data warehousing, ETL/ELT processes, and data pipeline orchestration
Strong programming and database experience with Python and SQL Server for data integration, transformation, and analytics
Experience building APIs, microservices, and event-driven architectures to support enterprise data platforms and applications
Knowledge of data quality, data governance, metadata management, and data lifecycle best practices
Experience leveraging AI-assisted engineering tools and practices, including GitHub Copilot, prompt engineering, AI-driven code generation, testing, and documentation
Experience developing and deploying cloud-native solutions using OpenShift Container Platform (OCP)

Responsibilities

Lead the design, development, and modernization of enterprise data, analytics, and reporting platforms
Provide technical leadership, architecture guidance, and mentorship while promoting engineering best practices and AI-assisted development
Design and build scalable data pipelines, data products, and batch, streaming, and event-driven integration solutions
Develop governed, reusable data assets and improve data quality, lineage, observability, and platform performance
Support cloud modernization initiatives and the migration of legacy data solutions
Build data architectures and semantic data layers that enable advanced analytics, Generative AI, LLM, and RAG capabilities
Enable Agentic AI and Context Engineering through discoverable, metadata-driven, and well-governed data assets
Partner with business, technology, and AI teams to deliver scalable, secure, and compliant data and analytics solutions
Establish governance standards, ensure adherence to security and regulatory requirements, and drive operational efficiency and cost optimization