Lead Software Engineer - Metrics and Payments Business Observability

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As a Lead Software Engineer - Metrics and Payments Business Observability at JPMorgan Chase, within the Commercial & Investment Banking – Data Analytics and 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 delivering 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, and and manages enterprise applications at scale to ensure robust and fault tolerant data processing and ingestion.
- 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
- Sets and upholds engineering standards for the team’s Spring Boot services (API design, error handling, security, logging, performance tuning, versioning)
- Ensures production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management and develops secure high-quality production code, and reviews and debugs code written by others
- Applies knowledge of SDLC toolchains, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- 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.
- 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

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 and 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 building highly scalable, robust and fault tolerant API (REST, GraphQL)
- Experience designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow
- Experience with AWS cloud platforms, including deploying and operating services in a cloud environment
- 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 understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Preferred qualifications, capabilities, and skills

- Experience with Agentic AI, LLMs, RAG architectures, vector databases, and embedding-based retrieval systems
- Experience with API development with focus on performance and low latency.
- Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)
- Experience with data observability, quality, and metadata management tools
- Experience with BI platforms (Tableau, Sigma), build tools (dbt, metric flow).
- Experience with data modeling techniques (star schema, snowflake), query optimization, and working with Clickhouse is a plus.
- Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)