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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Software Engineer - Data Engineer - **Company:** JPMorgan Chase & Co. - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Software Applications, Automation of Tests, Cloud Computing, Cloud Engineering, Software Quality, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Identity and Access Management, Python (Programming Language), Software Tools, Secure Coding, Software Engineering, Software Systems, Data Streaming, Strategies of Testing, Toolchain, Data Processing, Delivery Pipeline, Large Language Models, AWS Lambda, Amazon Virtual Private Cloud (VPC), Production Code, AWS Glue, Cloudwatch, Code Restructuring, Data Pipelines, Amazon Elastic Mapreduce (EMR), Amazon Redshift - **Published:** September 2, 2026 - **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210786394 ## About the Role * Formal training or certification on software engineering concepts and 5+ years applied experience * Proficient experience in system design, testing, and operational ownership * Advanced Python and cloud-native engineering on AWS (e.g., IAM, VPC, KMS, CloudWatch) * Proven delivery of production ETL/ELT pipelines (batch and/or streaming) on AWS using services such as AWS Glue, Amazon EMR, AWS Lambda, and orchestration via Amazon MWAA (Airflow) and/or AWS Step Functions * Strong data engineering fundamentals: CDC/incremental processing, backfills, idempotency, late-arriving data handling, and schema evolution * Data platform experience with AWS analytics and storage services (e.g., Amazon S3, Amazon Redshift, Amazon Athena, AWS Lake Formation/Glue Data Catalog) and streaming/messaging (e.g., Amazon Kinesis, Amazon MSK) * Data reliability practices: data quality controls, monitoring/alerting, CI/CD for pipelines (e.g., CodePipeline/CodeBuild), performance & cost optimization, and security/governance compliance (e.g., CloudTrail, least-privilege access) * 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 * Practical experience leveraging Large Language Models (LLMs) to accelerate advanced coding workflows ## Description As a Lead Software Engineer - Data Engineer at JPMorgan Chase within the Cloud Financial Management Technology group, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives., * Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems * Develops secure and high-quality production code, and reviews and debugs code written by others * 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. * 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, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture * Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies * Adds to team culture of diversity, opportunity, inclusion, and respect ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Got AI ideas but no money? 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