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 product ionize 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 JPMorgan

Chase 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 work streams, 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 Data Frame 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)…