Lead Software Engineer

JPMorgan Chase

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: OH
Posted: 70 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

How this pay compares to similar roles

Similar $202k

$187k 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.

Employer

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.

Most-posted roles

View all roles at JPMorgan Chase

At a glance

TL;DR · Lead Software Engineer

Role Posting Log in to save

Lead Software Engineer - Databricks/Spark/AWS is a core technical contributor within the Corporate Sector, Chief Technology Office team. You will lead the architecture and delivery of high-throughput, low-latency data pipelines while establishing lakehouse patterns using Delta Lake. Your daily responsibilities include managing Databricks cluster strategies, orchestrating jobs via Databricks Workflows, and designing secure ingestion frameworks using AWS services like S3, Glue, Lambda, Kinesis, and CloudWatch. You will build reusable libraries and APIs in Python or Java while driving the adoption of AI-assisted engineering practices to improve code quality and delivery speed. The role requires expertise in Apache Spark, SQL, Terraform, and CI/CD pipelines to solve complex data processing challenges. You will ensure data quality and governance through Unity Catalog while optimizing performance for batch and streaming ETL processes within a secure environment.

Skills

Databricks Apache Spark Python Java SQL AWS Delta Lake Terraform CI/CD S3 Glue Kinesis Kafka MSK Unity Catalog Lambda DynamoDB Git pytest JUnit

What you'll do

Lead the architecture and delivery of high-throughput, low-latency data pipelines using Databricks and Apache Spark.
Establish lakehouse patterns with Delta Lake to ensure performance at scale through ACID transactions and schema evolution.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Manage Databricks cluster strategies, including runtime selection, autoscaling, and configuration optimization.
Design secure data ingestion and transformation frameworks using AWS services like S3, Glue, Lambda, and Kinesis.
Perform Spark performance engineering by optimizing partitioning, shuffle tuning, and memory management to control costs.
Build reusable libraries and APIs in Python or Java while overseeing comprehensive unit and integration testing.
Implement CI/CD pipelines for data projects using Git workflows and Terraform infrastructure deployments.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
10+ years of professional software/data engineering experience including production work with Spark on Databricks or EMR.
Demonstrated experience leading the use of approved AI-assisted software development tools while ensuring security and performance standards.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs.
Proficiency in Python and/or Java for data processing, platform tooling, and automation.
Hands-on expertise with Databricks (Delta Lake, Unity Catalog, Workflows) and AWS services (S3, IAM, Glue, CloudWatch, Kinesis).
Proven track record architecting ETL/ELT pipelines, Spark performance tuning, and SQL data modeling.
Experience with CI/CD automation, Terraform infrastructure deployments, and security-first engineering practices.

Save