Leads development of secure, scalable data pipelines and software applications using Python, Spark, FastAPI, AWS, and Terraform. Designs, deploys, tests, troubleshoots, and maintains production data architectures, reviews code, improves operational stability, and evaluates architectural solutions with vendors and internal teams. Promotes responsible AI-assisted engineering practices, coaches team members on secure AI adoption, and contributes to agile team culture.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As an AWS Lead Software Engineer - Python, Big Data at JPMorgan Chase within the Corporate Technology-Digital Workflows team, you will be a seasoned member of an agile team, tasked with designing and delivering reliable data collection, storage, access, and analytics solutions that are secure, stable, and scalable. Your responsibilities will include developing, testing, and maintaining essential data pipelines and architectures across diverse technical areas, supporting various business functions to achieve the firm's business objectives.
Job responsibilities
- Builds data processing pipeline and deploys applications in production
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture - Adds to team culture of diversity, opportunity, inclusion, and respect
- 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
- 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 5+ years applied experience
- Experience on Bigdata, Data platforms and Data Engineering concepts
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- End to end understanding and hands on experience with Developing Data Pipeline Processes
- Programming experience in Python, FAST API, Spark
- Technically competent in various AWS Services – Lambda, Glue, Step Function, ECS, AWS Infrastructure provisioning through Terraform
- Demonstrated experience leading the effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting), with a strong understanding of responsible AI use in engineering workflows—including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security standards. Skilled at setting team expectations for validating AI outputs and coaching engineers on safe, compliant adoption within delivery practices.
- Ability to tackle design and functionality problems independently with little to no oversight
- Training in Generative AI solutions like NLP & LLM Models
- 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
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
- Training and Work experience in implementation of Generative AI solutions like NLP solutions involving – LLM Models, RAG, Prompt Engineering, AWS Bedrock
- Previous experience with Snowflake
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What you need to know about the Pune Tech Scene
Once a far-out concept, AI is now a tangible force reshaping industries and economies worldwide. While its adoption will automate some roles, AI has created more jobs than it has displaced, with an expected 97 million new roles to be created in the coming years. This is especially true in cities like Pune, which is emerging as a hub for companies eager to leverage this technology to develop solutions that simplify and improve lives in sectors such as education, healthcare, finance, e-commerce and more.
