Lead Data Engineer: ETL, PySpark & Cloud Data Apps

Fairygodboss; Columbus (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

JPMorgan Chase is seeking a Lead Software Engineer - Data Engineering to join the Consumer & Community Banking/Data Products team. You will design and deliver trusted data-driven technology products in a secure, scalable manner, contributing across multiple technical areas to support business objectives.

Responsibilities include building large-scale ETL pipelines, developing Python applications, and implementing ML-assisted engineering practices while upholding secure coding and CI/CD standards.

Qualifications

• 5+ years of software and data engineering experience.

Responsibilities

• Design, develop, and optimize large-scale ETL data pipelines.
• Build Python applications with modular code and automated tests.
• Develop distributed data processing with PySpark.
• Design end-to-end testing and validation.
• Implement workflow orchestration with Control-M or Airflow (MWAA).
• Develop cloud-native solutions using AWS services (Glue, Athena, Lambda, CloudWatch).
• Design data lake architectures using Iceberg or Delta Lake.
• Administer Snowflake including streams, tasks, roles, and warehouses.
• Participate in code reviews and promote CI/CD best practices.
• Leverage AI-assisted development tools while ensuring secure, compliant delivery.

Skills

Python

Java

PySpark

DBT

SQL

Airflow/MWAA

Control-M

AWS

Iceberg/Delta Lake

Snowflake

CI/CD

AI-assisted engineering

Education

Bachelor's degree in Computer Science or related field

Tools

DBT

Snowflake

AWS

Airflow/MWAA

Control-M

Iceberg/Delta Lake

PySpark

Job description

JPMorgan Chase is seeking a Lead Software Engineer - Data Engineering to join the Consumer & Community Banking/Data Products team. You will design and deliver trusted data-driven technology products in a secure, scalable manner, contributing across multiple technical areas to support business objectives.

Responsibilities include building large-scale ETL pipelines, developing Python applications, and implementing ML-assisted engineering practices while upholding secure coding and CI/CD standards.