Backend Software Engineer III - Python/JAVA

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As a Software Engineer III at JPMorganChase within the Consumer & Community Banking (Data Products), you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 

You’ll be a seasoned agile engineer designing and delivering secure, stable, scalable data products and pipelines that power market-leading customer experiences. Responsibilities span building modular Python services (reusable classes, logging, tests), distributed processing with PySpark, and modern ELT with dbt (macros, Jinja, seeds, custom tests). You’ll orchestrate workloads with Airflow (MWAA/Astronomer/Kubernetes), develop on AWS (Glue, Athena, Lambda, CloudWatch), and automate infrastructure using Terraform. You’ll work with lakehouse/open table formats (Iceberg/Hudi/Delta Lake) and Snowflake (streams, tasks, roles, warehouses), using Git, code reviews, and CI/CD. Exposure to Kafka/Flink and familiarity with AI/ML concepts (LLMs, prompt engineering, evaluation, embeddings/vector search, responsible AI) are a plus.

Job responsibilities
 

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Build production-grade Python applications using modular design, reusable classes, structured logging, and automated testing.
• Develop and optimize ETL/ELT pipelines across batch and distributed compute environments (e.g., PySpark).
• Author and maintain dbt assets: macros, Jinja templates, seeds, and custom test cases to enforce data quality.
• Orchestrate workflows with Airflow (MWAA, Astronomer, or Kubernetes) and ensure reliable scheduling, retries, alerting, and runbook readiness.
• Implement AWS-based data processing patterns using Glue jobs, Athena, Lambda, and CloudWatch for monitoring/observability.
• Automate infrastructure provisioning and deployment workflows with Terraform.
• Apply lakehouse concepts and open-table formats (Iceberg, Hudi, Delta Lake), including querying and managing large-scale datasets.
• Work with Snowflake features such as streams, tasks, roles, and warehouses for scalable data operations.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
   

Required qualifications, capabilities, and skills

• Formal training/certification in software engineering concepts and 3+ years of applied experience.
• Proficiency in Python and/or Java, including hands-on system design, application development, testing, and production support.
• Experience building solutions on AWS and working in a large corporate engineering environment.
• Understanding of SDLC, agile delivery, CI/CD, application resiliency, and security practices.
• Experience with modern databases and strong SQL/data querying skills.
• Exposure to streaming technologies such as Kafka and/or Flink.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
• Overall knowledge of the Software Development Life Cycle
   

Preferred qualifications, capabilities, and skills

• Familiarity with modern AI/ML workflows and concepts (prompt engineering, LLMs, model evaluation, embeddings/vector search, and responsible AI).
• Experience using AI tools to improve engineering productivity or build data-driven solutions.