About the role
structured by ORIDrive innovative technology solutions to support the KYC & Risk Assessment business, as part of an agile Data Engineering team If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place. As a Lead Software Engineer at JPMorgan Chase…
What you will do
- Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers
- Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions
- Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies
- Advises cross-functional teams on technological matters within your domain of expertise
- 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 establish
What they are looking for
- Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale
- Expert in one or more programming languages, particularly Python and/or Java
- Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
- Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
- Practical cloud-native experience (AWS, Azure, or GCP)
- Ability to present and effectively communicate with senior leaders and executives
Nice to have
- Experience with modern data platforms such as Databricks or Snowflake
- Deep hands-on experience with Spark/PySpark and other big data processing technologies
- Expertise in open-source table formats and catalog services such as Apache Iceberg
- Experience with LLM orchestration frameworks and model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)
Full posting text
Drive innovative technology solutions to support the KYC & Risk Assessment business, as part of an agile Data Engineering team
If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.
As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector, you provide expertise and engineering excellence as an integral part of an agile data engineering team. To enhance, build, and deliver a trusted market leading Global Know Your Customer (KYC) and Risk Assessment Data Platform in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios.
Job Responsibilities:
Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers
Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions
Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies
Advises cross-functional teams on technological matters within your domain of expertise
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.
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 at scale.
Required qualifications, capabilities, and skills
Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale
Expert in one or more programming languages, particularly Python and/or Java
Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
Practical cloud-native experience (AWS, Azure, or GCP)
Ability to present and effectively communicate with senior leaders and executives
Preferred qualifications, capabilities, and skills
Experience with modern data platforms such as Databricks or Snowflake
Deep hands-on experience with Spark/PySpark and other big data processing technologies
Expertise in open-source table formats and catalog services such as Apache Iceberg
Experience with LLM orchestration frameworks and model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)