AdTechTalent
Data Science7 days agoHybrid

Epsilon

Senior Data Engineer

databricksawssparksqlpythonlinuxkafkaairflowdockerkubernetesetleltdata engineeringcloud data platformdistributed systemsdata pipelinesad-tech

Key details

Salary

$89K – $165K

Employment type

Full-time

Seniority

Senior

Years experience

5-10

Location

Chicago, United States

Full job description

Senior Data Engineer role to build and operate cloud-native data pipelines and analytics systems using Databricks, AWS, Spark, SQL, and Python. Responsibilities include troubleshooting production issues, designing scalable data products, advancing platform reliability and scale, collaborating across teams, and driving continuous improvement. Requires 6-8 years experience in data engineering with strong skills in Databricks, AWS, SQL, Python, Linux, and cloud integration. Experience with Kafka, Airflow, Docker, Kubernetes, and databases is valued. Location: Chicago, Illinois. Salary range: $88,900 - $165,100.

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
  • Advance cloud data platform with AWS services improving scalability, observability, resilience, cost efficiency, and operational excellence
  • Apply deep expertise in Databricks, AWS, SQL, Python, Linux, and cloud integration patterns to solve complex engineering problems and influence architecture decisions
  • Collaborate with cloud architects, integration engineers, analysts, and product partners to deliver scalable and maintainable data solutions aligned to business outcomes
  • Continuously improve data engineering standards, developer productivity, automation, and platform capabilities
  • Influence technical decisions and contribute to evolving platform
  • Clearly articulate technical concepts and solutions to internal teams and partners

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
  • Strong communication, problem solving, and ownership skills
  • Ability to work effectively across teams in a high-performance engineering environment

Tech stack

DatabricksAWSSparkSQLPythonLinuxKafkaAirflowDockerKubernetesETLELT

Benefits

Flexible time off (FTO)15 paid holidaysPaid sick timeParental/new child leaveChildcare & elder care assistanceAdoption assistanceComprehensive health coverage401(k)Tuition assistanceCommuter benefitsProfessional developmentEmployee recognitionCharitable donation matchingHealth coaching and counseling

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