Agentic AI Data Lead Software Engineer

JPMorganChase•Columbus, OH

About The Position

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.

Requirements

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.
Extensive experience with AWS cloud services including S3, Athena, Glue, Lambda, Step Functions, IAM, KMS, and Terraform.
Deep understanding of data governance principles including metadata management, data lineage, access control (RBAC/ABAC), data classification, and policy enforcement.

Nice To Haves

Experience working with UI frameworks (React, Angular) will be an added advantage.
Basic knowledge of Snowflake (architecture, performance optimization, Tasks, Streams, Stored Procedures, Materialized Views, security model) is preferred, but not mandatory.
Experience with Grafana or equivalent observability platforms for custom dashboards, APM, SLA monitoring and alerting is a plus

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.