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 (sem