Resiliency Data Products - Data Engineer Vice President
Data Engineer (VP) on JPMorgan's Corporate Investment Bank Operational Resiliency team in London, designing and running production-grade data pipelines and governed data products with end-to-end lineage to analyze technology recovery risk. Core stack: SQL, Python, PostgreSQL/MySQL, MongoDB, Spark/Hadoop, and cloud-based distributed systems.
- Compensation
- Not disclosed
- Location
- London, England, United Kingdom
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Relevant experience in data engineering, analytics engineering, or data platform engineering across the data lifecycle
- Strong proficiency in SQL and hands-on programming experience in Python
- Practical experience with data modeling and data integration across multiple systems, including migration and complex data mapping
- Experience with relational databases (e.g., PostgreSQL or MySQL), including performance optimization and operational management
- Experience with non-relational database technologies (e.g., MongoDB) and associated query patterns
- Familiarity with large-scale data processing and analytics platforms (e.g., Apache Spark or Hadoop)
- Experience implementing data quality, metadata, and governance controls to improve reliability and auditability
- Understanding of modern distributed systems patterns (APIs, event streaming) and cloud-based environments
- Strong analytical and problem-solving skills with attention to detail and root-cause thinking
- Strong communication skills and ability to build partnerships across business and technology stakeholders
Nice to have
- Degree (or equivalent practical experience) in computer science, information systems, data science, or related field
- Experience with scenario generation or modeling approaches supporting resiliency and recovery risk analysis
- Exposure to statistical and analytical techniques, including data mining methods
- Experience producing data architecture artifacts (target-state diagrams, data flows, lineage views, conceptual/logical models)
- Industry accreditation such as TOGAF or cloud/solution architecture certifications
Full description
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