Lead Software Engineer, Data Platform

JPMorgan Chase

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 92 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $203k

$174k most similar roles pay here $231k

This listing doesn't post a salary. Most similar roles pay $192,050–$214,000.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Data Platform

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Lead Software Engineer - Data Platform joins the Commercial & Investment Banking – Data Analytics – Payments Technology team to build and deliver secure, scalable technology products. As a core technical contributor, you will design and maintain data pipelines and ETL/ELT workflows for batch and real-time processing while developing platform components like data cataloging, quality frameworks, and semantic layers with embedded governance. You will implement data modeling strategies to support analytics and partner with stakeholders to translate requirements into production-grade solutions. The role requires expertise in Python, Java, SQL, Spark, Flink, Kafka, and Airflow, alongside experience with Databricks, Kubernetes, and cloud services like AWS S3, Glue, Redshift, and Athena. You will also manage infrastructure using Terraform and Docker while navigating complex data mesh architectures to solve critical problems within the payments technology domain.

Skills

Python Java SQL Apache Spark Flink Kafka Airflow Databricks AWS S3 Glue Redshift Athena EMR Kubernetes Docker Terraform CI/CD dbt Tableau LLMs RAG Vector Databases

What you'll do

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink.
Develop data platform components including cataloging, quality frameworks, and semantic layers with embedded governance.
Implement data modeling strategies such as fact and dimensional models to support analytics and reporting.
Translate complex business requirements from stakeholders into production-grade technical solutions.
Write, review, and debug high-quality production code while ensuring system stability and security.
Automate the remediation of recurring issues to improve the operational stability of software applications.
Lead evaluation sessions with vendors and internal teams to assess architectural designs and technology applicability.
Lead communities of practice to drive the adoption of leading-edge technologies across the engineering organization.

What we're looking for

Formal training or certification in software engineering concepts.
5+ years of applied experience in software engineering or data platform development.
Advanced proficiency in Python, Java, and SQL.
Hands-on experience with distributed processing frameworks like Apache Spark and Flink.
Experience designing and operating data pipelines on Databricks using tools like Apache Airflow.
Proficiency with cloud data services such as AWS S3, Glue, Redshift, Athena, EMR, or Lake Formation.
Experience engineering production-grade data platforms on Kubernetes with open catalog integration.
Advanced understanding of agile methodologies including CI/CD, application resiliency, and security.

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