Lead Data Engineer

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Plano, TX
Employment: Full-time
Posted: 4 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $164k

$116k $214k

below market most similar roles pay here above market

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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 1219 open roles on FindRole.

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At a glance

TL;DR · Lead Data Engineer

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As a Lead Data Engineer within the Consumer and Community Banking team, you will design, develop, and maintain robust data pipelines and architectures. Your daily responsibilities include building scalable batch and streaming pipelines with high fault tolerance, managing workflow orchestration, and translating complex business requirements into technical solutions for data lakes and warehouses. You will drive data governance, mentor engineers, and perform advanced quantitative analysis on large datasets. The role requires expertise in AWS Data Lake, Snowflake, Redshift, BigQuery, Spark, Flink, Trino, SQL, and Kafka. You must also utilize enterprise-authorized AI capabilities for pipeline design and documentation while ensuring security. Technical skills include experience with Erwin, Iceberg, and Hudi. You will solve problems related to data modeling, cataloging, access control, and large-scale distributed data processing.

Skills

AWS Snowflake Spark Flink Trino SQL Kafka Redshift BigQuery Pub/Sub Data Lake Data Warehousing ETL Iceberg Hudi Erwin Agile Data Modeling Data Governance

What you'll do

Design, build, and optimize scalable batch and streaming data pipelines with fault tolerance and observability.
Develop and operate workflow orchestration to schedule and manage data movement and transformations.
Translate complex business requirements into technical solutions for data lake and data warehousing standards.
Use enterprise-authorized AI to accelerate data pipeline design, documentation, and test generation.
Build and maintain governance processes for data modeling, cataloging, ownership, and access control.
Provide technical leadership and mentorship on data publication best practices and standards.
Perform advanced quantitative analysis of large datasets to identify business trends.
Design and implement large-scale data solutions in cloud environments using AWS and Snowflake.

What we're looking for

Hold a Bachelor’s degree in Computer Science, Information Technology, or a related field.
Have 5+ years of experience in data engineering with deep AWS, Data Lake, and Snowflake expertise.
Have hands-on experience with modern data lake and warehousing technologies like Redshift, BigQuery, Snowflake, Spark, Flink, or Trino.
Exhibit proficiency in SQL and experience with data pipeline/ETL tools.
Have experience designing and building streaming pipelines using Kafka, Pub/Sub, or similar messaging systems.
Have experience with large-scale distributed data processing, performance tuning, and designing cloud-based data solutions.
Demonstrate experience using enterprise-authorized AI capabilities to support data engineering workflows with strong validation habits.
Have experience with data modeling in Erwin (preferred).
Have experience with table formats such as Iceberg or Hudi (preferred).