Lead Software Engineer - Data & AI Platform Engineer

Location: Jersey City, NJ, United States

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 – Data Analytics – Payments Technology 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

• 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)

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