JPMorgan Chase—Senior Lead Software Engineer Java FSD + AWS

Job description

Description: We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate Risk Technology, you are an integral part of an agile Data Platform and Strategy Team driving the design, development, and delivery of advanced data engineering solutions and market-leading technology products. With a strong SRE mindset, you will champion reliability, scalability, and operational excellence across multiple technical domains, ensuring our systems are robust, secure, and resilient. You will lead critical technology initiatives, proactively address system reliability challenges, and foster a culture of continuous improvement and automation, supporting the firm’s business objectives through innovative engineering leadership   Job 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 for data-intensive applications, and reviews and debugs code written by others - Drive the implementation of SRE best practices, including automated monitoring, alerting, and self-healing mechanisms to ensure high availability and reliability of data platforms, - Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets. - Lead root cause analysis and post-incident reviews, collaborating with other engineering team members to develop long-term solutions that prevent recurrence and improve system resiliency - Mentor and guide other engineering team members in adopting SRE principles, fostering a culture of reliability, automation, and continuous improvement  - Collaborate with product, engineering, and operations teams to define and measure service level objectives (SLOs), service level indicators (SLIs), and error budgets - 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 -   Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 10+ years applied experience - Proficiency in Engineering & Architecture, AI/ML with hands-on experience in designing, implementing, testing, and ensuring the operational stability of large-scale enterprise data platforms and solutions - Demonstrated expertise in applying SRE principles to drive reliability engineering, automation, and operational excellence within complex technical environment - Deep understanding of distributed systems, cloud-native architectures, and large-scale data processing, with hands-on expertise in Java, Python, and big data technologies (e.g., Spark/PySpark, Databricks, Snowflake) - Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs. - Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns. - Hands-on practical experience delivering system design, application development, testing, and operational stability - Experience in developing, debugging, and maintaining code (preferably in a large corporate environment) with one or more modern programming languages and database querying languages with good overlap of application & DB.  - Strong background in observability tools (e.g., Dynatrace, Splunk, Grafana), incident management, and performance optimization - Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Preferred qualifications, capabilities, and skills - Proficiency in automation, continuous delivery methods and all aspects of the Software Development Life Cycle - Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g. data engineering, cloud, artificial intelligence, machine learning, mobile, etc.) - Experience with modern data technologies such as Databricks or Snowflake. Experience in large scale data processing, using micro services, API design,  Kafka, Redis, MemCached, Observability (Dynatrace , Splunk, Grafana or similar), Orchestration (Airflow, Temporal) - Knowledge of the financial services industry and their IT systems