Lead Data Engineer

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Chicago, IL
Employment: Full-time
Posted: 4 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 $168k

$120k most similar roles pay here $217k

This listing doesn't post a salary. Most similar roles pay $129,032–$207,637.

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 1174 open roles on FindRole.

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At a glance

TL;DR · Lead Data Engineer

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As a Corporate Technology - Lead Data Engineer, you will join the Corporate Technology team to design, implement, and maintain robust data pipelines that collect, process, and store large volumes of data from diverse sources. You will ensure data timeliness, quality, and completeness while maintaining strict compliance with financial regulations and privacy requirements for data at rest and in transit. Your daily work involves building scalable distributed architectures using cloud-native technologies and services. You will utilize Java, Python, and SQL to develop production ETL/ELT pipelines involving batch and streaming processes. Key technical components include Spark or Flink, Kafka, Snowflake or Databricks, and various table formats like Delta or Iceberg. Additionally, you will engage with stakeholders to define technical targets, evaluate new technologies, and potentially incorporate AI/ML tools into the development lifecycle within a large-scale enterprise environment.

Skills

Python Java SQL Spark Flink Kafka Snowflake Databricks AWS GCP ETL ELT HDFS Iceberg Avro Parquet Vector Search

What you'll do

Design and develop scalable distributed architectures for data ingestion and processing using cloud-native technologies.
Build and maintain high-volume ETL/ELT pipelines for batch and streaming data from diverse sources.
Ensure all data solutions comply with residency, privacy, and financial security regulations.
Implement best practices for securing data at rest and in transit according to firm policies.
Define the technical target state of products and drive the execution of the engineering strategy.
Develop and optimize production pipelines using frameworks like Spark or Flink and streaming tools like Kafka.
Manage data modeling, partitioning, and clustering within Snowflake, Databricks, or similar lakehouse platforms.
Implement data quality checks, backfills, and observability reporting to ensure system reliability.

What we're looking for

Proficiency in Java and Python including sound testing and code review practices.
Expertise in SQL including joins, aggregations, subqueries, and window functions.
Ability to design, build, and optimize production ETL/ELT pipelines for batch and streaming using frameworks like Spark or Flink.
Hands-on experience with Kafka, including topics, keys, partitions, consumer groups, and schema registry basics.
Experience with data modeling, partitioning, clustering, and platforms such as Snowflake or Databricks.
Production experience with at least one major cloud provider (GCP/AWS) using native data services and cost-effective design.
Ability to ensure data security, quality, and compliance with residency and privacy regulations.
Experience in financial services, mentoring engineers, or familiarity with AI/ML technologies (preferred).