Epsilon seeks a Senior Data Engineer to build and operate cloud-native data pipelines and analytics systems using Databricks, AWS, Spark, SQL, and Python. The role involves troubleshooting production issues, optimizing performance, advancing platform scalability and reliability, collaborating across teams, and driving continuous improvement. Requires 6-8 years of experience in data engineering with strong skills in Databricks, AWS, SQL, Python, Linux, and cloud integration. Experience with Kafka, Airflow, Docker, Kubernetes, and NoSQL databases is a plus. Benefits include flexible time off, paid holidays, parental leave, health coverage, 401(k), tuition assistance, and more. Location: Chicago, Illinois, United States.
What you'll do
Identify and resolve production issues, optimize performance, and address bottlenecks in data processing
Design, build, and optimize cloud-native data pipelines using Databricks, Spark, SQL, and Python
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Requirements
BA/BS in Computer Science or related field
6-8 years of experience in data engineering, cloud data platform engineering, or large-scale data systems
Strong track record of delivering production-grade solutions using Databricks, AWS, SQL, Python, and modern ETL/ELT frameworks
Proficiency in Databricks, Spark, SQL, and Python
Hands-on experience with AWS cloud services, Linux, workflow orchestration, and modern data pipeline development in cloud-native environments
Deep understanding of distributed systems, cloud data architecture, data warehousing, and integration patterns
Experience with Kafka, Airflow, Docker, Kubernetes, relational and NoSQL databases is highly valued
Strong communication, problem solving, and ownership approach
Ability to work effectively across teams in a high-performance engineering environment
