Lead Software Engineer - Data Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Marketing Automation Platforms Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Lead, mentor, and grow a high-performing team of 5 – 7 engineers across multiple workstreams, fostering a culture of innovation, ownership, and technical excellence.

  
  
  

• Set the technical vision and engineering roadmap for the Data Products platform, aligning with firmwide priorities.

  
  
  

• Operate as a player-coach — providing hands-on architectural guidance while empowering the team to own and deliver independently.

  
  
  

• Drive cross-functional collaboration with platform teams, domain Data Product Owners, AI/ML teams and governance teams.

  
  
  

• Architect and own the end-to-end technical design of the Data Products Studio — a scalable, enterprise-grade platform that orchestrates the discovery, design, build, and productionization of data products from the CCB Data Lake and Snowflake.

  
  
  

• Design the platform's AI/Agentic AI layer, leveraging intent agents, NLP Text-to-SQL, Knowledge Graphs (KAG), RAG, Vector Databases, and Agent-to-Agent (A2A) communication to enable intelligent, automated data product creation and natural language interaction with the data estate.

  
  
  

• Establish and enforce architectural standards, design patterns, and engineering best practices across the team — ensuring scalability, security, resilience, and maintainability.

  
  
  

• Lead the design and development of Agentic AI capabilities that power the Data Products Framework — including autonomous discovery agents that profile and recommend data product candidates, design agents that auto-generate data contracts and schema recommendations, build agents that generate and optimize data pipelines, governance agents that auto-apply entitlements based on data classification, and quality agents that detect anomalies, drift, and trigger self-healing remediation.

  
  
  

• Architect the Agent-to-Agent communication layer enabling multi-agent orchestration across the data product lifecycle — from discovery through productionization.