Data Reliability Engineer
Cubist’s data services group is looking for a Data Reliability Engineer to join our dedicated team in New York. We are passionate about data and collaborate to build elegant, effective, scalable, and highly reliable solutions to empower predictive modeling in finance. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers globally.
About the role
This exceptional individual will join a global team of Data Reliability Engineers who play a vital role in ensuring the smooth day-to-day implementation of a large research infrastructure, and the live production trading of billions of dollars of capital across global capital markets, including equities, futures, options, and other financial instruments.
Responsibilities
- Level 1 support for thousands of mission-critical data pipelines.
- Day-to-day operations including data validation, data entry tasks, data cleaning, security audits, and reporting.
- Incident management including user communication and reporting for management.
- Developing tools, documentation, and processes to assist with daily operations and support.
- Support for complex permission and entitlements operations across many different systems.
Requirements
- Bachelor’s degree with a concentration in Computer Science or related discipline.
- 3+ years of experience working in a client-facing support or operations role.
- Experience working in Windows and Linux environments both on-prem and in AWS.
- Experience with SQL Server and Python.
- Financial industry experience preferred but not required.
- Attention to detail and a love of process.
- Strong organization, communication, and interpersonal skills.
- Commitment to the highest ethical standards.
Pay
The annual base salary range for this role is $100,000–$150,000 (USD), which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary based on geographic location, work experience, education, and skill level.