Lead Software Engineer - Data & AI Platform 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 JPMorgan Chase, within the Commercial & Investment Banking's Payments Technology team to build a Data & AI Platform powering analytics and automation at scale. You’ll design and implement modern data pipelines and platform capabilities on a cutting-edge stack, backed by strong governance across catalog, lineage, and data quality. You’ll lead an Agentic AI initiative to automate and optimize platform engineering and architecture, partnering with product and analytics teams to deliver production-grade solutions.

 Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems

• Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows for batch and real-time processing using Spark, Airflow, Kafka, and Flink

• Develops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with embedded governance, lineage, and compliance standards

• Implements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption

• Partners with analytics teams, product managers, and business stakeholders to translate data requirements into production-grade solutions

• Develops secure high-quality production code, and reviews and debugs code written by others

• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

• Leads development of the Agentic Autonomous Lakehouse capability - automating governed self-service pipeline provisioning and lakehouse operations (health/cost/performance analysis, best-practice enforcement)

• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

• Adds to team culture of diversity, opportunity, inclusion, and respect

 

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience

• Hands-on practical experience delivering system design, application development, testing, and operational stability

• Demonstrated professional experience focused on software engineering or data platform development

• Advanced in one or more programming languages(s); Python, Java and SQL

• Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink

• Solid understanding of data modeling techniques (star schema, snowflake) and query optimization 

• Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow

• Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)

• Experience engineering production-grade data platforms on Kubernetes with open catalog integration (e.g., Apache Iceberg, Unity Catalog, OpenMetadata) for scalable data discovery, lineage, and governance. 

• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• Experience developing Agentic AI, LLMs, RAG architectures, MCP, vector databases, and embedding-based retrieval systems

 

Preferred qualifications, capabilities, and skills
 

• Hands-on familiarity with Data Platform and transformation framework development

• Experience with data mesh or data product architectures

• Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)

• Experience with data observability, quality, and metadata management tools

• Experience with semantic layers, metrics stores, or BI platforms (Tableau, dbt Metrics)