Agentic AI Data Lead Software Engineer

Join our team as a Lead Data Products Software Engineer responsible for architecting, building, and scaling the Data Products Framework — a next-generation platform that enables users to discover, design, build, and productionize governed data products at enterprise scale. You will lead a team of engineers, driving the technical strategy and execution of a platform that orchestrates the end-to-end data product lifecycle leveraging AI/Agentic AI, policy-based governance, and cloud-native architectures on AWS. As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Marketing Process Automation 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. - 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. - Define the platform's integration architecture with various firmwide systems as appropriate. - 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. - Leverage RAG (Retrieval Augmented Generation) and Vector Databases to enable contextual, knowledge-grounded AI interactions with metadata, lineage, and data catalog information. - Implement NLP Text-to-SQL capabilities allowing business users to explore the CCB Data Lake and Snowflake using natural language, lowering the barrier to data product discovery. Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience - 10+ years of progressive experience in software engineering, data engineering, or platform engineering - Strong leadership experience in guiding and mentoring varying levels of Software Engineers - Proven track record of architecting and delivering large-scale, enterprise-grade data platforms or frameworks from concept through production in a large corporate environment - Deep hands-on expertise in Python, SQL, and at least one additional language (preferably Java 17+, Spring, Boot), with strong system design and distributed systems knowledge - Extensive experience designing, building, and optimizing ETL/ELT pipelines at scale, including batch and real-time data processing. - Strong proficiency in PySpark for distributed data processing, including DataFrame and Dataset APIs and Spark SQL. - Experience working with UI frameworks (React, Angular) will be an added advantage. - Extensive experience with AWS cloud services including S3, Athena, Glue, Lambda, Step Functions, IAM, KMS, and Terraform. - Basic knowledge of Snowflake (architecture, performance optimization, Tasks, Streams, Stored Procedures, Materialized Views, security model) is preferred, but not mandatory. - Deep understanding of data governance principles including metadata management, data lineage, access control (RBAC/ABAC), data classification, and policy enforcement. Preferred qualifications, capabilities, and skills - Experience with Grafana or equivalent observability platforms for custom dashboards, APM, SLA monitoring and alerting is a plus - Experience working with UI frameworks (React, Angular) will be an added advantage