Lead Data Engineer

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 79 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $164k

$118k $211k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$202,200.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Lead Data Engineer

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As a Lead Data Engineer within the Corporate Sector, you will join an agile team to build and deliver secure, stable, and scalable data collection, storage, access, and analytics solutions. You will be a core technical contributor responsible for maintaining critical data pipelines and architectures across various business functions. Your daily work involves building and optimizing batch and streaming pipelines, developing workflow orchestration using Apache Airflow, and modeling data for analytics using SQL. You will write production-grade Python and PySpark code while mentoring junior engineers. The role requires expertise in cloud-based data platforms like AWS, Google Cloud, or Azure, along with experience in large-scale distributed data processing and modern data warehousing. You will also utilize enterprise-authorized AI capabilities to accelerate data platform design and documentation.

Skills

Python PySpark SQL Apache Airflow AWS Azure Data Pipelines Distributed Data Processing Data Warehousing Object-Oriented Programming System Design Testing Strategies Streaming Data

What you'll do

Build and optimize batch and streaming data pipelines with strong performance, fault tolerance, and observability.
Develop and operate workflow orchestration tools like Apache Airflow to manage data movement and transformations.
Model and transform data using SQL to support business intelligence and reporting workloads.
Write production-grade Python/PySpark code with disciplined testing and maintainable object-oriented design.
Own critical data systems by improving reliability, scalability, security, and operational excellence.
Mentor junior engineers and influence technical direction through standards, reviews, and knowledge sharing.
Use enterprise-authorized AI capabilities to accelerate data platform design while ensuring data security and validation.

What we're looking for

Formal training or certification on data engineering concepts and 5+ years of applied experience.
Hands-on professional experience actively coding as a data engineer in an agile, fast-paced environment.
Strong software engineering fundamentals including system design, data structures, object-oriented programming, and testing strategies.
Experience building and operating cloud-based data platforms using major cloud services like AWS, Google Cloud, or Azure.
Experience with large-scale distributed data processing and performance tuning.
Experience with modern data warehousing/lakehouse technologies and strong SQL skills for transformation tooling.
Experience designing and operating orchestration pipelines using Airflow or similar tools.
Demonstrated experience using enterprise-authorized AI capabilities to support data engineering workflows with strong validation habits.