Join our Corporate Investment Bank Operational Resiliency team and help shape how we make confident, risk-informed decisions that strengthen business resilience. You will build trusted, well-governed data products that power analytics, scenario generation, and clear insight into technology recovery risk. You will partner across cybersecurity, engineering, controls, and business stakeholders to deliver pragmatic solutions with measurable outcomes. If you enjoy production-grade engineering and solving complex problems at scale, this role offers the chance to make a visible impact.

As a Resiliency Data Products - Data Engineer Vice President within Corporate Investment Bank Operational Resiliency, you will design, build, and run data products and pipelines that improve how we understand and manage technology resiliency and recovery risk. You will help us create high-quality, well-governed data with end-to-end lineage so teams can make better decisions with confidence. You will work in a collaborative, cross-functional environment where engineering discipline, controls, and operational outcomes matter.

Job responsibilities

• Design, build, and operate production-grade data pipelines that ingest, cleanse, transform, and aggregate data from multiple sources
• Build resilient data products that support lineage, traceability, and trusted analytics for resiliency and recovery risk use cases
• Develop and evolve logical and physical data models that represent user flows, system dependencies, resiliency signals, and risk measures
• Translate business, risk, and control requirements into technical designs and an achievable delivery plan
• Partner with architects, engineers, analysts, and stakeholders across a matrix organization to deliver outcomes end to end
• Implement and continuously improve data quality, metadata management, and governance controls to improve reliability and auditability
• Enable data lineage across sources, transformations, and curated outputs to support explainability and operational decision-making
• Apply modern architecture patterns to support scalable and, where needed, near real-time data requirements
• Use structured and non-relational database technologies to optimize performance and operational stability
• Embed engineering best practices such as version control, automated testing, code review, and continuous integration and delivery
• Produce clear updates on progress, risks, and dependencies and drive timely decisions to keep delivery on track

Required qualifications, capabilities, and skills

• Relevant experience in data engineering, analytics engineering, or data platform engineering across the data lifecycle from collection through modeling and analytics enablement
• Strong proficiency in Structured Query Language (SQL) and hands-on programming experience in Python
• Practical experience with data modeling and data integration processes across multiple systems, including migration and complex data mapping
• Experience working with relational database technologies (for example, PostgreSQL or MySQL), including performance optimization and operational management
• Experience with non-relational database technologies (for example, MongoDB) and associated query patterns
• Familiarity with large-scale data processing and analytics platforms (for example, Apache Spark or Hadoop)
• Experience implementing, or partnering closely on, data quality, metadata, and governance controls to improve reliability and auditability
• Understanding of modern distributed systems patterns (for example, application programming interfaces and event streaming) and cloud-based environments
• Strong analytical and problem-solving skills, with attention to detail and a focus on root-cause thinking
• Strong communication skills and the ability to build partnerships across business and technology stakeholders

Preferred qualifications, capabilities and skills

• Degree (or equivalent practical experience) in computer science, information systems, data science, or a related field
• Experience with scenario generation or modeling approaches that support resiliency and recovery risk analysis
• Exposure to statistical and analytical techniques, including familiarity with data mining methods
• Experience producing clear data architecture artifacts such as target-state diagrams, data flows, lineage views, and conceptual or logical models
• Industry accreditation such as TOGAF or cloud/solution architecture certifications