Lead Software Engineer, Databricks, Spark, AWS

JPMorgan Chase

Quick summary

Work type: On-site
Location: Plano, TXJersey City, NJ
Salary: $152,000–$215,000 / yr
Posted: 5 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $200k

This role $184k

$143k most similar roles pay here $234k

This role pays less than 71% of similar roles. Most pay $183,500–$215,500 — the shaded band above. At the midpoint, this role pays about $184k versus about $200k for comparable roles.

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 436 open roles on FindRole.

Listed pay typically runs $152,000–$215,000 across 230 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Databricks, Spark, AWS

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As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector's Chief Technology Office, you will lead an agile team responsible for enhancing and delivering high-throughput, low-latency data pipelines using Databricks and Apache Spark. Your daily tasks include establishing lakehouse patterns with Delta Lake, orchestrating jobs with Databricks Workflows, and integrating with AWS services such as S3, Glue, IAM, CloudWatch, Lambda, Kinesis, and Kafka for secure data ingestion and transformation. You will also drive performance engineering initiatives to optimize cost and ensure data quality through governance tools like Unity Catalog. Proficiency in Python or Java, hands-on experience with Databricks, and solid AWS expertise are essential, along with a strong background in Spark performance tuning and ETL/ELT pipeline architecture. This role is pivotal in supporting the financial institution's technology needs within a highly regulated environment.

Skills

Databricks Apache_Spark Delta_Lake AWS Python Java S3 IAM Glue CloudWatch Lambda Kinesis Unity_Catalog Terraform SQL CI/CD pytest JUnit VPC Subnets Routing Security_Groups

What you'll do

Lead the architecture and delivery of high-throughput data pipelines using Databricks and Apache Spark.
Establish lakehouse patterns with Delta Lake to ensure performance at scale in a secure environment.
Own Databricks cluster strategy, including runtime selection, autoscaling, and Spark configuration optimization.
Design secure data ingestion frameworks leveraging AWS services like S3, Glue, IAM, and CloudWatch.
Drive Spark performance engineering by optimizing partitioning strategies, file sizing, and memory control.

What we're looking for

10+ years of professional software/data engineering experience with substantial production work in Spark on Databricks or EMR.
Strong proficiency in Python and/or Java for data processing, platform tooling, and automation.
Hands-on expertise with Databricks (Delta Lake, Unity Catalog, Workflows, Repos/notebooks).
Solid AWS experience including S3, IAM, Glue, CloudWatch, Kinesis/MSK, DynamoDB.
Proven track record in architecting and operating ETL/ELT pipelines with schema design/evolution and reliability engineering.
Deep skills in Spark performance tuning and Databricks cluster setup/optimization.
Strong SQL and analytics data modeling expertise (dimensional/star schema; lakehouse best practices).

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