Lead Software Engineer
JPMorganChase · Bengaluru · Posted 2026-07-24
Tech stack: AWS, Java, SQL
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About the role
At JP Morgan Chase, we understand that customers seek exceptional value and a seamless experience from a trusted financial institution. That's why we launched Chase UK to transform digital banking with intuitive and enjoyable customer journeys. With a strong foundation of trust established by millions of customers in the US, we have been rapidly expanding our presence in the UK and soon across Europe. We have been building the bank of the future from the ground up, offering you the chance to join us and make a significant impact.
Responsibilities:
- Deliver end-to-end solutions in the form of cloud-native microservices architecture applications leveraging the latest technologies and the best industry practices
- Design, build, and operate scalable batch and streaming data pipelines (ETL/ELT) to ingest, process, and curate data for operational and analytical use-cases.
- Work with Big Data technologies and patterns (e.g., distributed processing and large-scale datasets) to ensure performance, reliability, and cost efficiency and develop and maintain curated analytics datasets / data products that enable reporting, KPI measurement, and dashboarding for business stakeholders.
- Partner with business analysts and product teams to define metrics, ensure data quality/lineage, and create trusted datasets that power dashboards and self-serve analytics.
- Use domain modelling techniques to allow us to build best in class business products.
- Structure software so that it is easy to understand, test and evolve.
- Build solutions that avoid single points of failure, using scalable architectural patterns and develop secure code so that our customers and ourselves are protected from malicious actors.
- Promptly investigate and fix issues and ensure they do not resurface in the future and make sure our releases happen with zero downtime for our end-users.
- See that our data is written and read in a way that's optimized for our needs and keep an eye on performance, making sure we use the right approach to identify and solve problems.
- Support the products you've built through their entire lifecycle, including in production and during incident management
- 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.
Qualifications:
- Formal training or certification in software engineering concepts and applied professional experience.
- Recent hands-on professional experience as a back-end software engineer.
- Strong Java experience (recent versions).
- Strong testing experience (unit, component, integration, end-to-end, performance, etc.).
- Hands-on data engineering experience building and operating production-grade data pipelines (batch and/or streaming) and integrations.
- Experience with Big Data / distributed processing and large-scale datasets (e.g., Spark/Hadoop ecosystems or equivalent managed services).
- Strong SQL and experience with analytics data modelling (e.g., dimensional/semantic modelling, KPI definitions).
- Experience with data quality controls, observability, and operational support for data pipelines.
- Experience with cloud technologies, distributed systems, RESTful APIs, and web technologies.
- Knowledge of messaging frameworks and experience operating/supporting mission-critical applications, including security considerations.
- Understanding of data stores, including relational databases and excellent written and verbal English communication skills.
- Experience in working in a highly regulated environment / industry
- Experience with AWS data services (e.g., S3, Glue, EMR, Athena, Redshift, Kinesis/MSK or equivalents) and cloud data lake / lakehouse patterns.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Experience with orchestration and CI/CD for data pipelines (e.g., Airflow or similar) and versioned data transformations.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Seasoned with cloud-native microservices architecture
- Proficient with AWS cloud technologies
Qualifications
- Formal training or certification in software engineering concepts and applied professional experience.
- Recent hands-on professional experience as a back-end software engineer.
- Strong Java experience (recent versions).
- Strong testing experience (unit, component, integration, end-to-end, performance, etc.).
- Hands-on data engineering experience building and operating production-grade data pipelines (batch and/or streaming) and integrations.
- Experience with Big Data / distributed processing and large-scale datasets (e.g., Spark/Hadoop ecosystems or equivalent managed services).
- Strong SQL and experience with analytics data modelling (e.g., dimensional/semantic modelling, KPI definitions).
- Experience with data quality controls, observability, and operational support for data pipelines.
- Experience with cloud technologies, distributed systems, RESTful APIs, and web technologies.
- Knowledge of messaging frameworks and experience operating/supporting mission-critical applications, including security considerations.
- Understanding of data stores, including relational databases and excellent written and verbal English communication skills.
- Experience in working in a highly regulated environment / industry
- Experience with AWS data services (e.g., S3, Glue, EMR, Athena, Redshift, Kinesis/MSK or equivalents) and cloud data lake / lakehouse patterns.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Experience with orchestration and CI/CD for data pipelines (e.g., Airflow or similar) and versioned data transformations.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Seasoned with cloud-native microservices architecture
- Proficient with AWS cloud technologies
Responsibilities
- Deliver end-to-end solutions in the form of cloud-native microservices architecture applications leveraging the latest technologies and the best industry practices
- Design, build, and operate scalable batch and streaming data pipelines (ETL/ELT) to ingest, process, and curate data for operational and analytical use-cases.
- Work with Big Data technologies and patterns (e.g., distributed processing and large-scale datasets) to ensure performance, reliability, and cost efficiency and develop and maintain curated analytics datasets / data products that enable reporting, KPI measurement, and dashboarding for business stakeholders.
- Partner with business analysts and product teams to define metrics
- ensure data quality/lineage, and create trusted datasets that power dashboards and self-serve analytics.
- Use domain modelling techniques to allow us to build best in class business products.
- Structure software so that it is easy to understand, test and evolve.
- Build solutions that avoid single points of failure, using scalable architectural patterns and develop secure code so that our customers and ourselves are protected from malicious actors.
- Promptly investigate and fix issues and ensure they do not resurface in the future and make sure our releases happen with zero downtime for our end-users.
- See that our data is written and read in a way that's optimized for our needs and keep an eye on performance, making sure we use the right approach to identify and solve problems.
- Support the products you've built through their entire lifecycle, including in production and during incident management
- 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.