About the role
from listingDrive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer at JPMorganChase within the Asset Management Core Data & Analytics Engineering organization, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. You will be part of a development team responsible for designing and implementing data and analytics solutions and AI ready capabilities on a federated data platform to support business initiatives. The role includes building performant, automated ingestion and transformation pipelines for internal and external data sources, developing new distribution channels, and delivering enhanced Data Products through a comprehensive data marketplace supporting Technology teams and business users, including Quantitative Research. In parallel, you will bring an understanding of how to design and implement an AI-ready data platform—helping embed reusable foundations that make the platform and its Data Products easier to understand, discover, trust, and consume for both traditional analytics and AI-enabled use cases (for example, strengthening semantic consistency, metadata and lineage, data quality signals, and modern consumption patterns) Job responsibilities Provides technical guidance and direction to support business and technical teams, contractors, and vendors delivering data ingestion, transformation, distribution, and data product capabilities on a federated data platform. Designs and develops secure, high-quality production code for scalable data pipelines, data services, and marketplace-facing Data Products; reviews, debugs, and improves code written by others. Leads design and implementation of reusable platform capabilities that make the platform and Data Products AI-ready (e.g., consistent meaning and definitions, strong metadata/lineage and quality signals, and consumption patterns that support both analytics and AI-enabled use cases). Drives decisions influencing product design, data product contracts, distribution approaches, and engineering processes to improve time-to-data, reliability, and consumer experience (including Quantitative Research). Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Establishes and promotes engineering standards and automation across the SDLC (testing, CI/CD, observability, performance), ensuring solutions are secure, stable, and scalable. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality and delivery outcomes, while setting measurable validation standards (secure coding, peer review, automated testing) and encouraging reuse of proven patterns within the SDLC/TLM toolchain. Acts as a function-wide subject matter expert in one or more focus areas (e.g., data engineering patterns, distributed processing, data product design, platform enablement), and contributes to the broader Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and extensive applied experience ( NAMR/APAC – India/ LATAM/ Hong Kong) Formal training or certification on software engineering concepts and advanced applied experience (EMEA/LATAM-Brazil) Singapore follow local country guidance Hands-on practical experience delivering system design, application development, testing, and operational stability Advanced in one or more programming language(s) including Python Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls. Ability to tackle design and functionality problems independently with little to no oversight Practical cloud native experience Preferred qualifications, capabilities, and skills Experience delivering Data Products via a data marketplace or self-service consumption model. Familiarity with platform foundations that improve trust and explainability (e.g., metadata, lineage, quality measurement, semantic consistency). Experience supporting quantitative or research consumers and performance-sensitive data access patterns. Some Cloud-based Data Analytics platform experience in Snowflake, Databricks or similar data cloud solutions. An AWS Certification is preferred, but not a perquisite.