Senior Lead Software Engineer - Data & AI Platform Engineer
Confirmed on the employer's own hiring board on Nov 10, 2026. First seen by Alion on Nov 5, 2026. JPMorganChase scores A on the Alion truth index.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Senior Lead Software Engineer at JPMorgan Chase, within the Commercial & Investment Banking's Payments Technology team to build a Data & AI Platform powering analytics and automation at scale. You’ll design and implement modern data pipelines and platform capabilities on a cutting-edge stack, backed by strong governance across catalog, lineage, and data quality. You’ll lead an Agentic AI initiative to automate and optimize platform engineering and architecture, partnering with product and analytics teams to deliver production-grade solutions.
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
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 development of the Agentic Autonomous Lakehouse capability - automating governed self-service pipeline provisioning and lakehouse operations (health/cost/performance analysis, best-practice enforcement)
Tackles highly complex, ambiguous engineering problems spanning multiple systems and teams, applying deep technical judgment to drive solutions through simplified, modular design and implementation.
- Evangelizing engineering patterns, reusable frameworks, and best practices to influence stakeholders and drive adoption of scalable strategic solutions
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
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.
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Experience developing Agentic AI, LLMs, RAG architectures, MCP, vector databases, and embedding-based retrieval systems
Preferred qualifications, capabilities, and skills
Hands-on familiarity with Data Platform and transformation framework 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)