As a Lead Software Engineer - Data and Payments Data Platform at JPMorgan Chase within the Commercial and Investment Banking - Data Analytics Payment team, youare 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm27s business objectives.

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
• Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows for batch and real-time processing using Spark, Airflow, Kafka, and Flink
• Develops data platform components including data cataloging, data quality frameworks, and semantic/metrics layers with embedded governance, lineage, and compliance standards
• Implements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption
• 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
• Partners with analytics teams, product managers, and business stakeholders to translate data requirements into production-grade solutions
• Develops secure high-quality production code, and reviews and debugs code written by others
• 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

• 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 5+ years of applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• 3+ years of professional experience focused on data engineering or data platform development
• Advanced in one or more programming languages(s); Python, Java and SQL
• Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink
• Solid understanding of data modeling techniques (star schema, snowflake) and query optimization
• Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow
• Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)
• 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
• Proficient in all aspects of the Software Development Life Cycle and demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

• Exposure to LLMs, RAG architectures, vector databases, and embedding-based retrieval systems
• Experience with data mesh or data product architectures
• Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)
• Experience with data observability, quality, and metadata management tools
• Experience with semantic layers, metrics stores, or BI platforms (Tableau, dbt Metrics)