Lead Software Engineer

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Plano, TX
Posted: 8 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $202k

$182k most similar roles pay here $230k

This listing doesn't post a salary. Most similar roles pay $192,050–$211,200.

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

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As a Lead Software Engineer - Databricks within the Corporate Technology team, you will serve as a core technical contributor responsible for architecting and delivering high-throughput, low-latency data pipelines. You will build and manage Lakehouse patterns using Delta Lake, oversee Databricks cluster strategies, and automate workflows integrated with AWS services. Your daily work involves designing secure ingestion frameworks, enforcing data governance via Unity Catalog or AWS Glue, and performing intensive Spark performance tuning to optimize cost and throughput. You will develop reusable libraries and APIs in Python and Java while implementing CI/CD pipelines using Git and Terraform. The role focuses on solving complex data engineering problems by building reliable ETL/ELT batch and streaming pipelines, ensuring high-quality data products through rigorous testing, and championing enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.

Skills

Databricks Apache Spark Delta Lake Python Java SQL AWS Terraform Airflow Unity Catalog CI/CD Git ETL ELT Data Modeling Streamlit React

What you'll do

Architect and deliver high-throughput, low-latency data pipelines using Databricks and Apache Spark.
Manage Databricks cluster strategies including runtime selection, autoscaling, and performance tuning for cost and throughput efficiency.
Implement Lakehouse patterns with Delta Lake to ensure performant and maintainable data platforms at scale.
Design secure ingestion and transformation frameworks using Databricks services and Airflow DAGs.
Enforce data quality, lineage, and governance using Unity Catalog or AWS Glue Catalog.
Develop reusable libraries, frameworks, and APIs in Python or Java with comprehensive test coverage.
Implement CI/CD pipelines using Git workflows and Terraform for infrastructure deployment and automated releases.
Lead the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
Advanced experience in software engineering and data engineering with production delivery using Apache Spark on Databricks or AWS EMR.
Advanced hands-on expertise in Databricks features including Delta Lake, Unity Catalog, Workflows, and cluster configuration/optimization.
Proven ability to architect and operate reliable batch and streaming ETL/ELT pipelines with schema design and reliability engineering.
Deep Spark performance tuning skills to diagnose bottlenecks and optimize for scalability, cost, and runtime efficiency.
Strong programming proficiency in Python and/or Java for data processing, platform tooling, and automation.
Expertise in SQL and analytics data modeling, including dimensional/star schema design and Lakehouse best practices.
Demonstrated experience leading the use of approved AI-assisted software development tools while ensuring security and quality standards.

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