Architect of Scalable Big Data Pipelines

JPMorganChase; Plano (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

JPMorganChase is seeking an experienced data engineering professional to design, implement, and optimize scalable data transformation pipelines and production-grade data platforms. You will drive AI-assisted development practices to improve code quality, speed, and operational outcomes, while ensuring secure coding and robust testing.

The role requires strong expertise in Python/Java, Spark, and modern data technologies, with responsibilities spanning SDLC tooling, CI/CD, and collaboration with

Qualifications

• Formal training or certification on Software engineering concepts and 5+ years applied experience.
• Advanced in one or more programming language(s) and framework(s), e.g., Python, Java, Spark, Databricks, Grafana, Prometheus, Elasticsearch, Cloudwatch, Spring Boot API, and containers.

Responsibilities

• Executes creative software solutions and troubleshooting with ability to think beyond routine approaches.
• Review, understand, code, optimize, and automate existing data transformation pipelines into scalable tasks.
• Plan, design, and implement data transformation pipelines and monitor operations of the data platform in production.

Skills

Python

Java

Spark

ETL pipelines

CI/CD

AI-assisted dev

Education

Software engineering training

Tools

Databricks

Git

Azure/AWS

Grafana

Prometheus

HBase

Cassandra

Job description

JPMorganChase is seeking an experienced data engineering professional to design, implement, and optimize scalable data transformation pipelines and production-grade data platforms. You will drive AI-assisted development practices to improve code quality, speed, and operational outcomes, while ensuring secure coding and robust testing.

The role requires strong expertise in Python/Java, Spark, and modern data technologies, with responsibilities spanning SDLC tooling, CI/CD, and collaboration with