Software Engineering Senior Manager – Quantitative Data & Analytics
for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Must not repeat job title. Must not include Job ID etc. Must remove boilerplate, EEO, legal, etc. Must preserve all substantive info.
We need to parse the posting.
Content sections:
- About this role: description paragraph(s). That's "About this role:" heading. Should we use About the role
? The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we map "About this role" to About the role
. Provide prose in
About the role
. Provide prose in. - Responsibilities: The bullet list after "In this role, you will:" includes many responsibilities. Those are list items. So we create
Responsibilities
then- ... each bullet.
- Requirements: There's "Required Qualifications:" list. That's qualifications. Could be under Requirements or Qualifications. The instruction includes both Requirements and Qualifications sections. We need to decide where to put required qualifications vs desired qualifications. Likely Required Qualifications go under Requirements (or Qualifications). Desired Qualifications could go under Skills? But we have Skills section. However we need to avoid thin sections. Could combine Required and Desired under Qualifications? But we have both sections available. Let's think: The posting includes "Required Qualifications:" and "Desired Qualifications:" plus many bullet items under each. Also there is a list of many desired qualifications (like experience building enterprise AI context layer etc). Also there is a list of many other desired qualifications after that (like knowledge of financial services data environments etc). Actually after Desired Qualifications there is a long list of bullet items (starting with "Experience building an enterprise AI context layer..." then many more). Then after that there is a list of many more items (starting with "Knowledge of financial services data environments...") Actually reading: After Desired Qualifications heading, there is a paragraph: "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." Then a series of sentences each starting with "Experience...". Those are desired qualifications.
Then later: "Ability to influence, collaborate, and build relationships across multiple levels of the organization." etc. That seems like more desired qualifications or skills.
Thus we could have:
-
Requirements
for Required Qualifications. -Qualifications
for Desired Qualifications (or maybe Skills). But we also have Skills section; maybe we could put some skill-based items under Skills. However the instruction says prefer a few substantive sections over many thin ones. So we could combine Required and Desired into one Qualifications section, but we have both headings available. However we must not emit a heading with filler/placeholder text. If we put both Required and Desired under Qualifications, we need to include all items. That's fine. But we also have Skills section; maybe we could put some skill-based items there (like "Strong leadership, communication, relationship management, and organizational skills." etc). However those are also qualifications. Given the instruction to prefer few substantive sections, we could have: - About the role - Responsibilities - Qualifications (combining required and desired) - Benefits/Pay/Schedule: The posting mentions hybrid work schedule, not eligible for visa sponsorship, relocation assistance not available. That's about schedule and maybe benefits? It says "This position offers a hybrid work schedule - ability to work in office". That's schedule info. Also "Relocation assistance is not available for this position" is a benefit? Could be considered under Benefits? But it's a negative. Also "This position is not eligible for Visa sponsorship". That's also a note. There's no explicit pay or benefits details. So we should include a Schedule section for hybrid work schedule. Benefits section only if there are actual benefits described (like health insurance, 401k). Not present. Pay section only if salary mentioned; not present. So we include Schedule. Thus sections: About the role, Responsibilities, Qualifications, Schedule. We must not include empty headings. Now we need to extract all substantive info and format. Let's extract About the role paragraph(s): "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." That's one paragraph. We'll put in. Now Responsibilities: list after "In this role, you will:" includes many items. Let's extract each bullet: - 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 That's a lot. We'll put each as
- . Now Qualifications: combine Required and Desired. Required Qualifications list: - 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 list (the long list). Let's extract each bullet: - 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. We should include all these as
- under Qualifications. Now Schedule: The posting says: "Job Expectations: This position offers a hybrid work schedule