Lead Data Architect – Modern Data Platforms & Integration at JPMorgan Chase, Chicago, IL
Lead Data Architect – Modern Data Platforms & Integration at JPMorgan Chase in Chicago, IL. Est. up to $160k. Apply with swipeandhired.
Your goal is to become a key player among imaginative thinkers who share a common commitment to continuous improvement and meaningful impact. Don’t miss this chance to collaborate with brilliant minds and shape the modern data capabilities powering Commercial Card and B2B Commerce.
As a Lead Data Architect at JPMorganChase within Commercial Card and B2B Commerce, you are an integral part of a team developing high-quality data architecture solutions for payments, client experiences, analytics, and AI. As a core technical contributor, you will define and drive modern data architecture across Data Lake, Data Mesh, Data Federation, and reusable Data Products, leveraging Databricks, Apache Spark, AWS Glue, AWS services, and a diverse database ecosystem. You will also help advance modern engineering practices, including spec-driven development and agentic build patterns, to accelerate delivery while improving quality, consistency, and control.
Job responsibilities
- Defines the data architecture target state for Commercial Card and B2B Commerce and drives execution of the strategy.
- Designs modern Data Lake, Data Mesh, Data Federation, and domain-oriented Data Product capabilities.
- Architects scalable data solutions using Databricks, Apache Spark, AWS Glue, AWS services, APIs, events, streaming, and modern data-sharing patterns.
- Defines standards for data modeling, metadata, lineage, data quality, governance, security, and compliance.
- Designs solutions across relational, NoSQL, distributed, and cloud-native database technologies.
- Drives adoption of spec-driven development and agentic engineering patterns to improve how data solutions are designed, built, tested, validated, and delivered.
- Leverages enterprise-authorized AI and agentic capabilities across the engineering lifecycle while maintaining appropriate human validation, security, traceability, resiliency, and auditability.
- Partners with product, engineering, business, and architecture teams to deliver strategic payment, data, and client capabilities.
- Provides technical leadership and architecture governance across multiple products and delivery teams.
- Evaluates emerging technologies and drives adoption of reusable, resilient, and scalable architecture patterns.
Required qualifications, capabilities, and skills
- Formal training or certification on data architecture concepts and 5+ years applied experience
- Proven experience in Data Architecture, Data Engineering, or Enterprise Data Management within large-scale environments
- Expertise in Data Lake, Data Mesh, Data Federation, Data Products, and modern data platform architectures
- Hands-on experience with Databricks, Apache Spark, AWS Glue, AWS services, and cloud-native data technologies
- Experience designing architectures across multiple database technologies and distributed data platforms
- Experience with APIs, event-driven architecture, streaming, data-sharing, and enterprise integration patterns
- Deep understanding of data modeling, metadata, lineage, data quality, governance, security, and compliance
- Experience with modern SDLC practices, automation, continuous delivery, and AI-assisted or agentic software engineering
- Ability to translate architecture and business intent into clear specifications that guide implementation, testing, and validation
- Strong stakeholder management, communication, technical leadership, and architecture governance skills
Preferred qualifications, capabilities, and skills
- Experience with Commercial Card, B2B payments, transaction processing, or commercial payments platforms.
- Experience with payment and transaction data, client integration, servicing, reporting, reconciliation, or analytics.
- Experience architecting large-scale data solutions using Databricks and AWS technologies.
- Experience implementing spec-driven development, agentic build/test workflows, or AI-enabled SDLC automation.
- Experience modernizing complex data estates spanning legacy, relational, NoSQL, distributed, and cloud-native platforms.
- Experience enabling enterprise data for advanced analytics, AI/ML, and client-facing capabilities.