Lead Software Engineer, DevOps & Infrastructure

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: OH
Posted: 3 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $178k

$136k most similar roles pay here $219k

This listing doesn't post a salary. Most similar roles pay $144,350–$211,200.

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.

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At a glance

TL;DR · Lead Software Engineer, DevOps & Infrastructure

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Lead Software Engineer: DevOps & Infrastructure joins the Employee Platforms, Workforce Experience Tech team to design, develop, and troubleshoot software components within a secure, stable, and scalable environment. This role involves creating high-quality production code, reviewing peer contributions, and driving the adoption of enterprise-authorized AI-assisted engineering practices to improve delivery speed and operational outcomes. The engineer will build and maintain ETL/ELT data pipelines using Python, SQL, and distributed processing technologies like Apache Spark, PySpark, and Delta Lake within a lakehouse architecture. Key responsibilities include managing data warehousing concepts such as dimensional modeling and query optimization while integrating data from APIs, message streams, and cloud storage. The role requires expertise in Databricks, CI/CD practices, and SDLC methodologies to solve complex problems regarding data governance, lineage, and automated remediation of recurring system issues.

Skills

Python SQL Databricks Apache Spark PySpark Delta Lake Lakeflow ETL ELT RAG CI/CD Agile SDLC GitHub Copilot Claude Code Prompt Engineering Data Warehousing infrastructure-as-code

What you'll do

Design, develop, and troubleshoot complex software components to build secure, stable, and scalable technical products.
Write high-quality production code while reviewing and debugging code written by other team members.
Drive the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Implement automated testing, peer reviews, and consistent validation standards for all software components.
Identify and automate the remediation of recurring issues to improve the operational stability of systems.
Lead evaluation sessions with vendors and internal teams to assess architectural designs and technical feasibility.
Develop and maintain production data pipelines using Python, SQL, and distributed processing technologies like Spark.
Manage data governance, lineage, and access controls within a lakehouse architecture.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience in system design, application development, testing, and operational stability.
Experience designing, developing, and operating ETL/ELT data pipelines using Python, SQL, and distributed data-processing technologies.
Strong understanding of data warehousing concepts including dimensional modeling, partitioning, and query optimization.
Experience developing production data solutions with Databricks, Apache Spark or PySpark, Delta Lake, and lakehouse architecture.
Knowledge of data governance, lineage, access controls, schema management, and production monitoring.
Experience integrating data from relational databases, APIs, files, message streams, and cloud-based storage platforms.
Strong knowledge of SDLC, Agile delivery methodologies, CI/CD practices, and secure software development principles.
Experience using enterprise-authorized AI-assisted software development tools to improve code quality and troubleshooting.
Proficiency in Python (Go is a plus) (preferred).
Databricks certification or equivalent experience delivering production-grade lakehouse solutions (preferred).

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