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# Senior Data Engineer

- **Company:** Epsilon, Inc.
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $88,900.0 - $165,100.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Big Data, Cloud Computing, Cloud Database, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Warehousing, Linux, Distributed Systems, Python (Programming Language), NoSQL, Cloud Services, SQL Databases, Workflow Management Systems, Data Processing, Cloud Platform System, System Availability, Apache Spark, Kubernetes, Information Technology, Apache Kafka, Data Management, Data Pipelines, Docker, Databricks
- **Published:** June 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ea8c814217405873

## About the Role

Do you have experience in Spark?, Do you have a Bachelor's degree?, * 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 working with Databricks, Spark, SQL, and Python from previous professional positions.
 * 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, and enterprise-scale data platforms is highly valued.
 * Strong communication, problem solving, and ownership approach, with the ability to work effectively across teams and contribute in a high-performance engineering environment

Why you might stand out from other talent:

 * Exposure to modern data tools and frameworks such as Kubernetes, Docker, and Airflow (a plus).
 * Familiarity with the internet/digital advertising ecosystem is a plus.

## Description

* Troubleshooting: Identify and resolve production issues, optimize performance, and address bottlenecks in data processing.
 * Build Scalable Data Products: Design, build, and optimize cloud-native data pipelines using Databricks, Spark, SQL, and Python to support large-scale ingestion, transformation, and delivery of high-value data across the platform.
 * Own Platform Reliability and Scale: Advance our cloud data platform with AWS services and strong engineering field, improving scalability, observability, resilience, cost efficiency, and operational excellence.
 * Lead with Technical Depth: Apply deep expertise in Databricks, AWS, SQL, Python, Linux, and cloud integration patterns to solve complex engineering problems and influence architecture decisions.
 * Partner Across Functions: Collaborate with cloud architects, integration engineers, analysts, and product partners to deliver data solutions that are scalable, maintainable, and aligned to business outcomes.
 * Drive Continuous Improvement: Continuously improve on data engineering standards, developer productivity, automation, and platform capabilities by bringing forward modern tools, patterns, and guidelines. Influencing technical decisions and contributing to our evolving platform.
 * Communication : Clearly articulate technical concepts and solutions to internal teams and partners, fostering a collaborative environment.
 * Troubleshooting: Identify and resolve production issues, optimize performance, and address bottlenecks in data processing.


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