Lead Software Engineer

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Wilmington, DE
Employment: Full-time
Posted: 111 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $197k

$129k $246k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $167,612–$226,350.

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 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 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 Sector’s Enterprise Technology team, you will join an agile team to build and deliver trusted, scalable technology products. You will lead the architecture and delivery of high-throughput, low-latency data pipelines using Databricks and Apache Spark. Your daily responsibilities include establishing Lakehouse patterns with Delta Lake, managing cluster strategies, and orchestrating automated workflows. You will design secure ingestion frameworks, enforce data governance using Unity Catalog, and perform deep Spark performance tuning. Technical requirements include proficiency in Python, Java, and SQL, alongside experience with AWS, Terraform, and CI/CD pipelines. You will solve complex data engineering problems by building reusable libraries, optimizing costs, and implementing secure, AI-assisted engineering practices to ensure reliable ETL/ELT processing across various business functions.

Skills

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

What you'll do

Lead the architecture and delivery of high-throughput, low-latency data pipelines using Databricks and Apache Spark.
Establish and evolve Lakehouse patterns with Delta Lake to ensure performant and maintainable data platforms.
Own Databricks cluster strategy, including runtime selection, autoscaling, and Spark configuration.
Orchestrate and automate data pipelines using Databricks Workflows and AWS eventing services.
Design secure ingestion and transformation frameworks to produce trusted datasets using Airflow and Unity Catalog.
Drive Spark performance engineering to optimize job throughput, cost, and memory control.
Build and maintain reusable libraries and APIs in Python or Java with comprehensive test coverage.
Implement CI/CD for data projects using Git-based workflows and Terraform-based infrastructure deployments.

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 and/or AWS EMR.
Advanced hands-on Databricks expertise across Delta Lake, Unity Catalog, Workflows, Repos/notebooks, and SQL Warehouses.
Proven ability to architect and operate reliable ETL/ELT data pipelines for batch and streaming with schema design and reliability engineering.
Deep Spark performance tuning skills to diagnose bottlenecks and optimize jobs for scalability, cost, and runtime efficiency.
Strong programming proficiency in Python and/or Java for data processing, platform tooling, and automation.
Strong SQL and analytics data modeling expertise, including dimensional/star schema design and Lakehouse best practices.
Demonstrated experience leading the use of approved AI-assisted software development tools and coaching teams on responsible AI security.