Lead Software Engineer - Data Platform

JPMorgan Chase & Co.; Jersey City, Northern (NJ, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in Jersey City is seeking a Lead Software Engineer to advance data analytics within the Payments Technology team. You will be a core technical contributor delivering trusted, scalable software across the firm.

The role centers on building data pipelines, implementing data models, and partnering with product managers to translate data needs into production solutions while upholding governance, security, and reliability.

Qualifications

• 5+ years of applied software engineering or data platform experience.
• Hands-on experience delivering system design, application development, testing, and operational stability.
• Demonstrated professional experience focused on software engineering or data platform development.
• Advanced in Python, Java and SQL.
• Hands-on experience with Spark and Flink.
• Solid understanding of data modeling techniques (star/snowflake) and query optimization.
• Experience with Databricks and Airflow.
• Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR).
• Experience deploying data platforms on Kubernetes with open catalog integration.

Responsibilities

• Executes creative software solutions and troubleshooting with non-routine approaches.
• Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink.
• Develops data platform components including cataloging, quality frameworks, governance and lineage.
• Implements data modeling strategies to support analytics and reporting.
• Partners with analytics teams and stakeholders to translate data requirements into production-grade solutions.
• Develops production-grade secure code and reviews code written by others.
• Leads evaluation sessions with vendors and internal teams on architectures and applicability.
• Leads communities of practice across Software Engineering to promote new technologies.

Skills

Python

Java

SQL

CI/CD

Agile methodologies

Education

Software engineering certification

Tools

Apache Spark

Flink

Apache Airflow

Databricks

Kubernetes

Terraform

Docker

OpenMetadata

Iceberg

Unity Catalog

Job description

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, youare 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 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
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Preferred qualifications, capabilities, and skills

• Experience with Agentic AI, LLMs, RAG architectures, vector databases, and embedding-based retrieval systems
• Hands-on familiarity with Internal Developer Portals such as Backstage — including service catalog management, software templating, and plugin 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)