Software Engineering Senior Manager – Quantitative Data & Analytics

About this role: 

Wells Fargo is seeking a Software Engineering Senior Manager – Quantitative Data & Analytics to lead a team of engineering professionals supporting the modernization and transformation of the Wealth & Investment Management (WIM) analytics ecosystem. This leader will be responsible for building and developing a high-performing engineering organization focused on delivering scalable, secure, and reliable data solutions that power enterprise reporting, analytics, and business intelligence capabilities. 

You will provide strategic direction for data engineering initiatives, drive modernization of legacy platforms, establish governance and engineering best practices, and partner closely with business and technology leaders to deliver high-value solutions.  You will also lead the build-out of an AI context layer (the semantic layer, business ontology, and context library that gives AI tools and analysts a trusted, governed understanding of analytics data). You will oversee the large-scale data and analytics platforms that power it. 

The ideal candidate will possess strong people leadership skills, deep technical expertise in data engineering, and the ability to execute complex initiatives while developing talent and fostering a culture of innovation, accountability, and continuous improvement. 

In this role, you will: 

• Manage, coach, and develop a team or teams of experienced data engineers and engineering managers in roles with moderate complexity and risk, and support of enterprise data solutions. 
• Ensure adherence to the Banking Platform Architecture, and meeting non-functional requirements with each release 
• Partner with, engage and influence architects and experienced engineers to incorporate Wells Fargo Technology technical strategies, while understanding next generation domain architecture and enable application migration paths to target architecture; for example cloud readiness, application modernization, data strategy 
• Establish and execute strategic priorities that align data engineering capabilities with business objectives and long-term technology roadmaps. 
• Drive modernization efforts by transforming legacy reporting and data-processing environments into scalable, governed, and reusable data platforms. 
• Oversee the design and implementation of enterprise-scale data pipelines, data models, data integration solutions, and analytics platforms. 
• Lead the design and build-out of an AI context layer (semantic layer, business ontology, context library, and governed metadata). It should let AI assistants, agents, and self-service users work with WIM data accurately and safely, and support change impact analysis. 
• Operate and continuously improve large-scale data and analytics platforms, with clear standards for reliability, performance, observability, and cost. 
• Evaluate and adopt new AI and data tools, such as AI-assisted engineering, automated metadata harvesting, and ontology and knowledge graph platforms, to accelerate delivery and scale the context layer. 
• Ensure engineering standards, controls, governance practices, and operational processes are consistently applied across the organization. 
• Partner with technology leaders, architects, product owners, and business stakeholders to prioritize work, define requirements, and deliver business outcomes. 
• Lead resource planning, workload management, budget oversight, and talent development initiatives to support organizational goals. 

• Identify opportunities to improve efficiency, reduce technical debt, eliminate redundant data assets, and optimize data processing capabilities. 
• Drive adoption of modern data engineering practices, including data orchestration, automation, monitoring, and cloud-based technologies. 
• Ensure compliance with enterprise risk, security, data management, and regulatory requirements. 
• Manage delivery of multiple initiatives while balancing competing priorities, deadlines, and stakeholder expectations. 
• Foster a culture of collaboration, innovation, inclusion, and continuous learning across the team. 
• Interpret, develop and ensure security, stability, and scalability within functions of technology with moderate complexity, as well as identify, manage and mitigate technology and enterprise risk 
• Collaborate with, partner with and influence Product Managers/Product Owners to drive user satisfaction, influence technology requirements and priorities in the product roadmap, promote innovative and intelligent solutions, generate corporate value and articulate technical strategy while being a solid advocate of agile and DevOps practices 
• Manage allocation of people and financial resources to ensure commitments are met and align with strategic objectives in technology engineering 
• Hire, build and guide a culture of talent development to have the skills required to effectively design and deliver innovative solutions for product areas and products to meet business objectives and strategy, as well as conduct performance management for engineers and managers 

Required Qualifications: 

• 7+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education 
• 7+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, or education. 
• 3+ years of management or leadership experience 
• 3+ years of experience operating large-scale enterprise data and analytics platforms (for example, data warehouses, data lakes or lakehouses, ETL/ELT pipelines, and BI environments)
• 2+ years of experience using modern data and AI tools to build AI context layers (for example, semantic layers, business ontologies, knowledge graphs, or governed metadata) that ground AI and analytics solutions in enterprise data

Desired Qualifications: 

• Experience building an enterprise AI context layer (semantic layer, business ontology, context library, and governed, versioned business definitions) that gives AI assistants, agents, and analysts a trusted, consistent view of enterprise data. 
• Experience designing business ontologies and knowledge graphs for financial services domains
• Experience with metadata management, business glossaries, data catalogs, and lineage tools and using that metadata to power AI context and change impact analysis. 
• Experience using emerging AI tools to accelerate data engineering and ontology development, such as AI coding assistants, automated metadata harvesting, and LLM-assisted semantic mapping and documentation. Experience grounding generative AI, agents, and natural-language analytics in enterprise data using techniques such as retrieval-augmented generation (RAG), GraphRAG, vector search, and Model Context Protocol (MCP), including testing outputs for accuracy. 
• Experience running large-scale data and analytics platforms in production, including observability, SLAs, incident and problem management, capacity planning, and cost optimization. 
• Knowledge of responsible AI, model risk, and data privacy practices for AI solutions in financial services, including controlling what data AI tools can access. 
• Experience leading enterprise data modernization, analytics transformation, or large-scale reporting platform initiatives. 
• Experience with cloud-based data platforms and modern data architectures. 
• Experience with enterprise data lake, data warehouse, ETL/ELT, and data orchestration technologies. 
• Experience supporting business intelligence and analytics platforms such as Power BI, Tableau, or similar technologies. 
• Knowledge of financial services data environments, governance standards, and regulatory requirements. 
• Experience developing reusable and governed data assets that support self-service analytics. 
• Experience with Agile delivery methodologies and product-based technology organizations. 
• Experience implementing data governance, data quality, risk management, and operational controls. 
• Proven ability to build, lead, and retain high-performing teams. 
• Strong executive presence and ability to communicate effectively with senior leadership. 
• Bachelor's degree or higher in Computer Science, Information Systems, Engineering, Data Science, or a related field 
• Ability to influence, collaborate, and build relationships across multiple levels of the organization. 
• Experience partnering with business and technology stakeholders to deliver strategic initiatives and technology solutions. 
• Experience leading teams responsible for the design, development, and implementation of enterprise data solutions. 
• Experience building and supporting large-scale data pipelines, data integration frameworks, and data platforms. 
• Experience with data modeling, database technologies, data warehousing, and enterprise reporting architectures. 
• Experience managing multiple priorities, complex projects, and technology deliverables in a fast-paced environment. 
• Strong leadership, communication, relationship management, and organizational skills. 

Job Expectations: 

• This position offers a hybrid work schedule - ability to work in office 
• This position is not eligible for Visa sponsorship 
• Relocation assistance is not available for this position 

Posting End Date:

28 Sep 2026

*Job posting may come down early due to volume of applicants.

We Value Equal Opportunity

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

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