Software Engineer II: Data Engineer

JPMorgan Chase & Co.•New York, NY

4h•$118,750 - $150,000

About The Position

As a Data Engineer II at JPMorgan Chase within the Consumer & Community Banking Connected Commerce – Banking Payments organization, you serve as a seasoned member of an agile team responsible for designing and delivering trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable manner. You develop, test, and maintain critical data pipelines and data architectures across multiple technical domains and business functions, ensuring high-quality, reliable data capabilities that support and advance the firm’s strategic business objectives.

Requirements

Formal training or certification on Software Engineering concepts and 2+ years applied experience
Working understanding of NoSQL databases with Advanced at SQL (e.g., joins and aggregations)
Experience with statistical data analysis and ability to determine appropriate tools and data patterns to perform analysis
Experience in ETL process/Advance concepts
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in AWS, design, implementation, and maintenance of data pipelines using Python and PySpark (secondary alternative: Java)
Experience in performance and tuning to ensure jobs are running at optimal levels and no performance bottleneck
Proficiency in Unix scripting, data structures, data serialization formats such as JSON, AVRO, Protobuf, or similar, big-data storage formats such as Parquet, Iceberg, or similar, data processing methodologies such as batch, micro-batching, or stream, one or more data modelling techniques such as Dimensional, Data Vault, Kimball, Inmon, etc., Agile methodology, TDD or BDD and CI/CD tools
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.

Nice To Haves

Python Advance development skills / Kafka & S3 integration in Performance optimization, Lambda, ECS, EKS, Kinesis, CloudWatch
Experience in carrying out data analysis to support business insights
Strong in PySpark, AWS, Terraform & Snowflake, GitHub Copilot, Airflow, Kubernetes

Responsibilities

Supports review of controls to ensure sufficient protection of enterprise data
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems.
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development.
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems.
Proactively identifies hidden problems, patterns in data, and uses these insights to drive improvements to coding hygiene and system architecture.
Contributes to software engineering communities of practice and events that explore new and emerging technologies
Updates logical or physical data models based on new use cases
Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
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.