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Software Engineer II – Data Engineering (Python/AWS)

JPMorgan Chase
Glasgow, GBR
Full-time
Mid
Onsite
Discovered 3 days ago
awscloudwatchdatabricksecspandaspostgresql

Full Job Posting

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

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