Why This Role Stands Out
Elevate your career as a Data Engineer at IMC, a leader in global financial markets, where you'll architect cutting-edge Big Data solutions and build robust data pipelines with a competitive salary of $175,000 - $225,000. This hybrid role is perfect for experienced engineers passionate about innovation and seeking significant professional growth in a dynamic, collaborative environment. Apply now to join a forward-thinking team and make a substantial impact.
Quick Overview
Salary
$175k - $225k/yr
Seniority
Mid Senior
Work mode
Hybrid
Location
Chicago, IL, United States
Posted
1 week ago
DockerSQLFlinkMachine LearningApacheApache SparkBashDatabricksHadoopJavaKafkaKubernetesPython
Job Description
We are seeking a dedicated and experienced Data Engineer to join our Chicago team. The ideal candidate is energized by working in a cutting-edge environment that enables IMC to continue to be at the forefront of the evolving global financial markets.
Core Responsibilities
Skills and Experience:
#LI-DNP
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range
$175,000-$225,000 USD
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
Core Responsibilities
- Architect, develop and deploy our Big Data environment (Kafka, Hadoop, Dremio, etc.)
- Build, deploy, and monitor our data processing pipelines (Java, Python, Spark, Flink)
- Collaborate with development teams on data modeling, data ingestion, and capacity planning
- Work with users to ensure data integrity and availability
- Act as a Big Data SME and consult on a variety of data-related questions from users and developers
Skills and Experience:
- 5+ years experience working in a mature data engineering environment
- 3+ years of experience building Kafka streaming applications and/or maintaining Kafka clusters
- 2+ years of experience building applications/pipelines with Big Data backends (S3, HDFS, Databricks, Iceberg, etc)
- Experience with Apache Spark, Apache Flink or similar tools
- Strong Java, Python, and SQL development skills
- Experience with common data-science toolkits, especially python-based
- Hands-on experience with Kubernetes and Docker
- Experience with monitoring tools such as PrometheGrafana, Alert Manager, Alerta and OpsGenie
- Strong statistical analysis skills
- Demonstrated ability to troubleshoot and conduct root-cause analysis
- Unix scripting experience (bash, python, etc.)
#LI-DNP
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range
$175,000-$225,000 USD
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
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