Lead Software Engineer: DevOps & Infrastructure

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you. As a Lead Software Engineer at JPMorgan Chase, within the Employee Platforms, Workforce Experience Tech team, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. Job responsibilities Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems Develops secure and high-quality production code, and reviews and debugs code written by others 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. 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 Required qualifications, capabilities, and skills 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. Hands-on 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, star and snowflake schemas, slowly changing dimensions, partitioning, and query optimization. Experience developing production data solutions with Databricks, Apache Spark or PySpark, Delta Lake, Lakeflow pipelines, Lakeflow Jobs, and lakehouse architecture. Knowledge of data governance, lineage, access controls, schema management, data quality, and production monitoring. Experience integrating data from relational databases, APIs, files, message streams, and cloud-based storage platforms. Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database query languages. Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices. Strong knowledge of the Software Development Life Cycle (SDLC), Agile delivery methodologies, CI/CD practices, application resiliency, and secure software development principles. Preferred qualifications, capabilities, and skills Experience with AI-powered coding assistants such as GitHub Copilot and Claude Code. Familiarity with prompt engineering, embeddings, and RAG pipelines. Experience building operational copilots or chatbots for runbooks or troubleshooting. Proficiency in Python (Go is a plus). Experience with Databricks Unity Catalog, Structured Streaming, data governance, and performance optimization. Familiarity with cloud data platforms and infrastructure-as-code or deployment automation for data workloads. Databricks certification or equivalent experience delivering production-grade lakehouse solutions.