Lead Software Engineer - Big Data(Data Engineer), Python, and AWS

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the CIB, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology data products in a secure, stable, and scalable way. As a core technical contributor,  you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

- 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

- Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way

- Uses enterprise-authorized AI capabilities within the work environment to accelerate data products and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.

- Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision

- Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations.

- Develops secure and 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

Required qualifications, capabilities, and skills

- Formal training or certification on data engineering concepts and 5+ years applied experience

- Proficient at processing and analyzing large datasets using Big Data technologies, including Apache Spark, across data platforms comprising of Redshift, SQL DB, RDS and Databricks.

- Design, development, and implementation of scalable data pipelines and data matching solutions on AWS, utilizing Python for data engineering tasks.

- Strong experience in setup and configuration of services such as S3,  Step Functions, EMR, Glue, Lambda, RDS, EventBridge, Step-Functions and API Gateway for scripting, automation, and orchestration of data workflows.

- Strong hands-on experience with Databricks and distributed data processing technologies.

- Understanding of data architecture, data modeling, data integration, and data product principles.

- Experience designing and operating large-scale data platforms and data products in complex enterprise environments.

- Development of semantic layer for data consumption using Iceberg tables and APIs.

- Implementation of continuous deployment and integration processes using Infrastructure as Code tools, including Terraform.

- Troubleshoot and resolve issues related to application code and data workflows.

- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.

- Working experience with both relational and NoSQL databases

- Advanced in one or more programming language(s)

- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Preferred qualifications, capabilities, and skills

- Experience working with modern enterprise data platforms and cloud-native data architectures.

- Experience supporting real-time, analytical, or AI/ML-oriented data use cases.

- Databricks, Snowflake, AWS cloud, or Data Engineering certifications