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Lead Data Engineer
Manager
Big Data
Bigdata
Cloud
Cloud Data Engineering
Cloud Data Platform
Cloud Data Warehouse
Cloud Data Warehouse
Cloud Platform
Cloud Platforms
Data
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Engineering
Data Engineering Lead
Data Integration
Data Lake
Data Lakehouse
Data Pipeline
Data Pipelines
Data Platform
Data Processing
Data Warehouse
Data Warehousing
Database
Databases
Databricks
ETL
Flink
Google Cloud Dataflow
Informatica
Information Technology (IT)
Integration
Kafka
Lead Data Engineering
Programming Language
Programming Languages
Snowflake
Spark
SQL
Stream Processing
Streaming Data
Job Description
Join JPMorganChase’s Corporate Technology team in Chicago, IL (onsite) as a Lead Data Engineer. In this role, you will design and deliver scalable data ingestion and processing capabilities, helping keep data timely, accurate, complete, and aligned with data residency, privacy, and security requirements. The position also emphasizes technical leadership, collaboration with stakeholders, and continuous improvement through evaluation of new technologies.
Salary: USD 133,000 - 175,000 per year.
What you’ll do
- Design and develop scalable, secure distributed architectures for data ingestion and processing using appropriate cloud-native technologies and services.
- Design, implement, and maintain data pipelines to collect, process, and store large volumes of data from multiple sources, ensuring timeliness, quality, and completeness.
- Ensure data solutions comply with data residency and privacy regulations, and apply best practices for securing data in transit and at rest in line with financial regulations and firm-wide policies.
- Collaborate with technical teams and business stakeholders to discuss and propose technical approaches for current and future needs.
- Define the technical target state for your product and drive execution against the strategy.
- Evaluate technology recommendations and provide feedback on new tools and approaches.
- Execute hands-on software design and development to deliver creative solutions.
What you bring
- Comfortable with Java/Python, including testing and code review practices.
- SQL expertise, including joins, aggregations, subqueries, and window functions.
- Ability to design, build, and optimize production ETL/ELT pipelines for batch and streaming using frameworks such as Spark, Flink, or Dataflow.
- Hands-on experience with Kafka fundamentals including topics, keys, partitions, and consumer groups, along with at-least-once semantics and schema registry basics.
- Data modeling plus partitioning and clustering experience.
- Hands-on experience with Snowflake, Databricks, or similar platforms, and cloud storage or HDFS.
- Production experience with at least one major cloud provider (GCP or AWS) using native data services; FinOps-aware with cost-effective design.
- Experience implementing data quality checks and backfills, incorporating SLIs with observability and reporting, and working with lakehouse platforms and table formats such as Delta, Iceberg, Avro, and Parquet, including time-travel.
Preferred qualifications
- Experience with Kafka, Flink, or other streaming technologies.
- Familiarity with AI/ML concepts including LLMs, prompt engineering, vector search, and responsible AI practices, plus experience using AI-assisted software development tools such as GitHub Copilot or Claude.
- Financial services industry experience and understanding of large-scale enterprise data environments.
- Experience mentoring engineers and leading technical delivery initiatives.
Technologies you may work with
- Java, Python, SQL
- Spark, Flink, Dataflow
- Kafka, schema registry
- Snowflake, Databricks
- HDFS, GCP, AWS
- Delta, Iceberg, Avro, Parquet
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