Senior Manager of Software Engineering

JPMorgan Chase

Quick summary

Work type
On-site
Location
Plano, TX
Posted
1 day ago

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How this pay compares to similar roles

Similar $219k
$178k most similar roles pay here $258k

This listing doesn't post a salary. Most similar roles pay $192,050–$246,150.

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 439 open roles on FindRole.

Listed pay typically runs $148,625–$212,500 across 228 roles with salary data.

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At a glance

TL;DR · Senior Manager of Software Engineering

As a Manager of Software Engineering at JPMorganChase within the Corporate Sector’s Enterprise Technology team, you will lead an agile group responsible for developing high-throughput, low-latency data pipelines using Databricks and Apache Spark. Your daily tasks include establishing lakehouse patterns with Delta Lake, managing Databricks clusters, orchestrating jobs with Databricks Workflows, and ensuring robust data quality and governance through Unity Catalog. You will also drive performance engineering initiatives to optimize cost and efficiency while building reusable libraries in Python or Java. This role requires extensive experience with Spark on Databricks, strong proficiency in Python/Java for data processing, and a deep understanding of CI/CD practices and automation tools like Git and Terraform. Additionally, you must have expertise in AWS networking, security-first principles, and observability for large-scale data systems.

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 for large datasets.
  • Own Databricks cluster strategy including runtime selection, autoscaling, and Spark configuration optimization.
  • Design secure data ingestion frameworks and orchestrate jobs using Databricks Workflows and Airflow.
  • Drive Spark performance engineering by optimizing partitioning strategies and file sizing for cost efficiency.
  • Build reusable libraries in Python or Java and oversee unit testing and integration testing.
  • Implement CI/CD pipelines for data projects, including automated deployments and code reviews.

What we're looking for

  • 10+ years of professional software/data engineering experience with Spark on Databricks or EMR.
  • Hands-on expertise in Databricks (Delta Lake, Unity Catalog, Workflows, Repos/notebooks).
  • Proven track record in ETL/ELT pipeline architecture and operation.
  • Deep skills in Spark performance tuning and Databricks cluster optimization.
  • Strong proficiency in Python and/or Java for data processing and automation.
  • Experience with CI/CD tools (Git workflows, pytest/JUnit) and Terraform for infra deployments.
  • Security-first mindset with roles management, secret handling, encryption, and network controls.

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