Senior Lead Software Engineer - Python/PySpark on Databricks

JPMorgan Chase

Wilmington (DE)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Job summary

JPMorganChase Corporate Technology is seeking a Lead Software Engineer - Python/PySpark/Databricks/AWS to join an agile team delivering trusted market-leading technology products in a secure, scalable way. As a core technical contributor, you will design and implement production-grade code, lead architectural discussions, and champion responsible AI practices across the SDLC.

This role demands 5+ years of engineering experience with Databricks on AWS and a track record of delivering reliable,

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Experience leading effective use of approved AI-assisted software development tools with code review, testing, and security.
Strong understanding of responsible AI use, data sensitivity, secure handling, and resiliency.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Experience building and operating Databricks Lakehouse solutions on AWS (S3, IAM, KMS, VPC, logging).
Experience with Delta Lake and Apache Spark on Databricks, performance optimization.
Experience delivering batch and streaming data pipelines with governance/security controls in Databricks.
Advanced in Python and PySpark.
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
Architect Databricks Lakehouse solutions with bronze/silver/gold layering; scalable ingestion from AWS sources.

Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Develops secure high-quality production code, and reviews and debugs code written by others
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.