Lead Software Engineer - Data Platform

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As a Lead Software Engineer at JPMorgan Chase, within the Commercial & Investment Banking – Data Analytics – Payments Technology team, 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. 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 firm’s 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
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.
Design, build and manage enterprise applications as scale to ensur robust and fault tolerant data processing and ingestion.
Lead the design and evolution of event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns).
Implements data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption
Partners with analytics teams, product managers, and business stakeholders to translate requirements into production-grade solutions.
Set and uphold engineering standards for the team’s Spring Boot services (API design, error handling, security, logging, performance tuning, versioning).
Ensure production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management.
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
Demonstrated professional experience focused on software engineering or data platform development
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
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)
Experience engineering production-grade data platforms on Kubernetes with open catalog integration (e.g., Apache Iceberg, Unity Catalog, OpenMetadata) for scalable data discovery, lineage, and governance.
Preferred qualifications, capabilities, and skills
Experience with Agentic AI, LLMs, RAG architectures, vector databases, and embedding-based retrieval systems
Hands-on familiarity with Internal Developer Portals such as Backstage — including service catalog management, software templating, and plugin development
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)
Experience in designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow is a plus.