Lead Data Engineer - Databricks Lakehouse on AWS

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorganChase is seeking a Lead Data Engineer to join Corporate Technology. You will lead multi-team data platform delivery, architect scalable data pipelines, and own critical pipelines across business functions on a Databricks Lakehouse hosted on AWS.

The role emphasizes security, reliability, and governance. Responsibilities include designing bronze/silver/gold data layers, implementing streaming and batch ingestion, and mentoring engineers in Spark/Databricks best practices to meet firm

Qualifications

• 7+ years in data engineering with leadership experience.
• Hands-on Databricks Lakehouse on AWS.
• Deep Delta Lake, Spark and performance tuning.
• Experience with streaming and batch pipelines.
• Strong AWS fundamentals for data platforms.
• Knowledge of data governance and security controls.
• Ownership of reliability: monitoring, incident response, RCA.

Responsibilities

• Lead delivery/architecture for multi-team data platforms.
• Maintain and optimize critical data pipelines and architectures.
• Build and operate Databricks Lakehouse on AWS.
• Ensure secure, scalable data collection, storage, and analytics.
• Drive reliability with monitoring, incident response, and RCA.

Skills

Data engineering

Lead delivery

Team leadership

Databricks Lakehouse

Delta Lake

Spark on Databricks

Structured Streaming

AWS fundamentals

Data governance

Monitoring & incident response

Tools

Databricks

Delta Live Tables

AWS (S3, IAM, KMS)

Terraform

Unity Catalog

Spark

Delta Lake

Job description

JPMorganChase is seeking a Lead Data Engineer to join Corporate Technology. You will lead multi-team data platform delivery, architect scalable data pipelines, and own critical pipelines across business functions on a Databricks Lakehouse hosted on AWS.

The role emphasizes security, reliability, and governance. Responsibilities include designing bronze/silver/gold data layers, implementing streaming and batch ingestion, and mentoring engineers in Spark/Databricks best practices to meet firm