What are the responsibilities and job description for the Lead Software Engineer| Risk Data Platform & Strategy position at JPMorgan Chase?
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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
- Drives the implementation of SRE best practices, including automated monitoring, alerting, and self-healing mechanisms to ensure high availability and reliability of data platforms
- Leads 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
- Mentors and guides 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
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5 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)
- 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