Software Engineer II – Data Engineering (Python/AWS)

Are you passionate about building data solutions that power real business decisions at scale? At JPMorganChase, you'll work alongside talented engineers in a collaborative, agile environment where your contributions directly impact the reliability and performance of critical data systems. We invest in your growth — offering access to cutting-edge tools, cloud technologies, and AI-assisted engineering practices that keep you at the forefront of the industry.

As a Software Engineer II at JPMorganChase within the Market Data Engineering team in Glasgow, you will design, build, and operate secure and scalable data pipelines and services across AWS and cluster platforms. You will contribute to a high-performing agile team, delivering solutions that are stable, testable, and production-ready. This role offers the opportunity to deepen your expertise across the modern data engineering stack while working on problems that matter to the firm and its clients.

Job responsibilities

• Design, build, and support ETL/ELT pipelines and data services on AWS, ensuring solutions are secure, stable, and scalable

• Develop and maintain data workflows using AWS Glue, including job authoring, data cataloging, and orchestration patterns

• Build and query data stores on Amazon Aurora PostgreSQL, applying sound data modelling principles and performance-aware SQL

• Contribute to Databricks workloads and integrations, supporting development-to-production practices across notebooks and jobs

• Write high-quality, secure code primarily in Python, with JavaScript where appropriate, and troubleshoot issues across upstream and downstream dependencies

• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards

• Apply 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

• Analyze large and diverse datasets to identify problems and contribute to decision-making that supports stable, high-quality application development

• Collaborate with technical and non-technical partners to communicate progress, surface risks, and deliver solutions with a secure-by-design mindset

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and expanding applied experience

• Hands-on experience in system design, application development, testing, and operational stability

• Experience developing, debugging, and maintaining code in a large-scale environment using modern programming languages and database querying languages

• Familiarity with Software Development Life Cycle practices, including exposure to agile methodologies, CI/CD pipelines, resiliency, and security principles

• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs

• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations

Preferred qualifications, capabilities, and skills

• Experience with Python for data engineering, including data manipulation and pipeline development using libraries such as pandas

• Practical knowledge of PySpark, including Spark SQL and performance-aware transformations

• AWS data engineering experience, including Lambda/Glue/DataLake/ECS/IAM/CloudWatch etc aware development and operational monitoring patterns

• Proficiency in SQL for analytics and pipeline validation, with PostgreSQL experience a plus

• Experience working with Databricks notebooks and jobs across development-to-production workflows

• An awareness of Market Data and Enterprise Level data security and access tracking