Resiliency Data Products - Data Engineer Vice President

JPMorgan Chase · London

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

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Join CIB Operational Resiliency: build governed data products for risk insights, partner across teams, drive measurable impact.

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