Software Engineer II – Python / Databricks

JPMorgan Chase & Co.; Auchentibber; On-site; GBP 60,000 - 90,000; Full time

Job summary

JPMorganChase seeks a Software Engineer II within Investment Banking Data Products to design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable.

You will develop, test, and maintain data pipelines and architectures to support various business functions, collaborating with engineers to drive innovation and uphold a culture of excellence and continuous improvement.

Qualifications

• Formal training or certification on software engineering concepts.
• Strong working knowledge of cloud-based data services (especially Glue jobs and Federated Data Lake), unified analytics platforms, and Python.
• Experience across the data lifecycle including ingestion, transformation, storage and access patterns.
• Advanced proficiency in SQL including joins and aggregations, with understanding of NoSQL databases.
• Significant experience with statistical data analysis and choosing tools/patterns for analysis.
• Experience using cloud services for developing, deploying and managing applications at scale.
• Familiarity with software development lifecycle tools for CI/CD, unit testing and performance testing.
• Knowledge of enterprise AI capabilities to support data pipelines with validation and data sensitivity awareness.
• Ability to review and validate AI-assisted code and recommendations with security in mind.

Responsibilities

• Develop workflows and ETL pipelines using Python and Databricks for scalable data solutions.
• Support review of controls to protect enterprise data across the data lifecycle.
• Implement data security using entitlements frameworks.
• Update data models based on evolving business requirements.
• Apply SQL expertise with complex joins and aggregations; leverage NoSQL for varied access patterns.
• Promote reuse-first, AI-assisted practices to improve data pipeline quality and traceability.
• Utilize enterprise AI capabilities to accelerate data pipeline design and documentation with proper data handling.

Skills

Python

SQL

Data Pipelines

Databricks

NoSQL

Glue / Federated Data Lake

AI-assisted practices

CI/CD

Data security

Tools

Glue

Federated Data Lake

CI/CD pipelines

NoSQL databases

Job description

Are you ready to shape the future of data engineering at JPMorganChase? Join a dynamic team where your unique skills will help build innovative solutions and contribute to a winning culture. You'll have opportunities for career growth, collaborate with talented professionals, and make a real impact on our business objectives. Your expertise will empower our teams and drive success across the firm.

As a Software Engineer II at JPMorganChase within Investment Banking Data Products, you will design and deliver reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. You will develop, test, and maintain essential data pipelines and architectures, supporting various business functions to achieve the firm's goals. Working alongside talented engineers, you will use your skills to drive innovation and help shape our team culture — one built on excellence, collaboration, and continuous improvement.

Job responsibilities

• Develop workflows and extract, load, and transform pipelines using Python and Databricks to support scalable and reliable data solutions
• Support the review of controls to ensure sufficient protection of enterprise data across the data lifecycle
• Implement data security using entitlements frameworks to safeguard sensitive information
• Update logical and physical data models based on evolving business use cases and requirements
• Apply SQL expertise — including complex joins and aggregations — and leverage working knowledge of NoSQL databases to support diverse data access patterns
• Apply reuse-first, AI-assisted practices to strengthen software development lifecycle quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability, auditability, and alignment to resiliency and security expectations
• Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline design and documentation, validating outputs and handling data according to sensitivity and security requirements

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and expanding applied experience
• Good working knowledge of cloud-based data services (especially Glue jobs and Federated Data Lake), unified analytics platforms, and Python
• Experience across the data lifecycle, including ingestion, transformation, storage, and access patterns
• Advanced proficiency in SQL, including joins and aggregations, with a working understanding of NoSQL databases
• Significant experience with statistical data analysis and the ability to determine appropriate tools and data patterns for analysis
• Experience utilizing cloud services for developing, deploying, and managing applications at scale
• Good understanding and working knowledge of software development lifecycle tools used for configuration management, continuous integration and delivery pipelines, unit testing, regression testing, and performance testing
• Working knowledge of using enterprise-authorized AI capabilities within the work environment to support software engineering workflows with strong validation habits and awareness of data sensitivity
• Ability to review and validate AI-assisted code and technical recommendations before use, escalating when uncertain and following security and data handling requirements

Preferred qualifications, capabilities, and skills

• Familiarity with standardized data layer practices such as Medallion architecture
• Exposure to relational database platforms and cloud data warehousing solutions
• Curiosity and foundational understanding of generative AI, large language models, and AI/ML solutions
• Skills in designing efficient data models, including normalization, denormalization, and schema design, with an understanding of relational and star schemas