Vista previa de la oferta
Software Engineer II: Data Engineer
mid · Technology / Software Development
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
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.
Job 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.
Required qualifications, capabilities, and skills
- 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.
Preferred qualifications, capabilities, and skills
- 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