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

JPMorgan Chase & Co.; Plano (TX); On-site; USD 140,000 - 200,000; Full time

Job summary

JPMorganChase within the CIB is seeking a Lead Software Engineer to architect and deliver trusted data products at scale. You will maintain critical data pipelines and architectures across multiple business functions, within an agile team, to support the firm's data-driven objectives.

The role emphasizes building secure, production-grade code, deploying with IaC (Terraform), and leveraging Spark, Databricks, and AWS services to enable real-time data processing and analytics while ensuring

Qualifications

• Formal training or certification in data engineering concepts and 5+ years applied experience.

Responsibilities

• Executes creative software solutions, design, development, and troubleshooting for scalable data platforms.
• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way.
• Uses enterprise-authorized AI capabilities to accelerate data products, validate outputs, and handle data with sensitivity and security.

Skills

Spark

Python

AWS Cloud

Data Engineering

CI/CD

Security

Databricks

Tools

Terraform

Job description

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