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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.
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