Lead Software Engineer

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Chicago, IL
Posted
3 days ago
Freshness
Confirmed live 2 days ago

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Salary context

How this pay compares to similar roles

Similar $204k
$187k most similar roles pay here $230k

This listing doesn't post a salary. Most similar roles pay $192,050–$216,075.

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

Lead Software Engineer - DataBricks, Spark, Terraform serves as a core technical contributor within the Corporate - Employee Platforms team. This role involves defining the technical roadmap for a Databricks lakehouse platform, managing ingestion, storage, modeling, and serving while ensuring high availability and performance. The engineer will design curated datasets using Delta Lake, implement governance via Unity Catalog, and manage cost observability. Day-to-day responsibilities include building pipelines, performing Spark and SQL tuning, and leading the adoption of AI-assisted development tools like GitHub Copilot to improve code quality and delivery speed. Required skills include proficiency in PySpark, Scala, SQL, Terraform, and CI/CD practices. The role addresses complex data engineering challenges including batch and stream ingestion, infrastructure as code, and maintaining security standards within a regulated environment while mentoring engineers on best practices for scalable data platform capabilities.

What you'll do

  • Define and drive the technical roadmap for the Databricks lakehouse platform including ingestion, storage, modeling, and serving.
  • Design curated datasets using Delta Lake and dimensional modeling while enforcing consistent naming and partitioning practices.
  • Build reliable systems by establishing SLOs, runbooks, alerting, and incident response for pipelines and SQL workloads.
  • Implement security governance through Unity Catalog, least-privilege access controls, and data classification.
  • Optimize Spark/SQL workloads to improve performance and manage costs through cluster and warehouse tuning.
  • Establish engineering standards for code quality, CI/CD, testing, and the creation of reusable libraries.
  • Lead the adoption of AI-assisted development tools while ensuring secure coding practices and high-quality output.
  • Coach engineers and lead design reviews to translate business requirements into scalable data platform capabilities.

What we're looking for

  • 7+ years of experience in data engineering or platform engineering.
  • Hands-on experience with Databricks in production environments.
  • Proficiency in Spark (PySpark/Scala) and SQL, including performance tuning and troubleshooting.
  • Experience building and operating data platforms involving batch/stream ingestion, transformation frameworks, and orchestration.
  • Experience with Data Lake technologies (Databricks, Snowflake, or AWS) and table optimization techniques like partitioning and Z-ORDER.
  • Familiarity with Unity Catalog or equivalent governance tooling for permissions, lineage, and auditing.
  • Solid software engineering fundamentals including Git, CI/CD, automated testing, code reviews, and modular design.
  • Experience leading the use of approved AI-assisted development tools while ensuring security and quality standards.
  • Knowledge of Databricks features like Workflows, Delta Live Tables, Structured Streaming, and SQL Warehouses (preferred).
  • Experience with transformation frameworks like dbt and semantic layer patterns (preferred).
  • Experience with Infrastructure-as-code such as Terraform (preferred).
  • Experience in regulated-data environments and enterprise data governance programs (preferred).

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