Data Operations Analyst - Reference Data

We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance.

Cubist’s data services group is looking for Data Operations Analyst to join our dedicated team. 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 in the world.

This exceptional individual will join a global team of analysts who monitor, validate, and support Security and Issuer reference data within the firm’s central reference data system.

RESPONSIBILITIES 

• Troubleshoot and resolve data discrepancies, monitor vendor SLAs, and communicate data issues to internal stakeholders.


• Implement proactive measures to identify and resolve data issues automatically.


• Handle internal client requests and inquiries, ensuring transparent support and leading communication on any SLA breaches.


• Assist in the onboarding of new datasets, validation rules, and user interface improvements, as well as participate in testing.


• Develop and document standardized processes for data support, monitoring, and quality assurance.


• Collaborate with global team members to ensure seamless transitions between support regions.


• Liaise with Business, Technology, and Operations leadership to ensure alignment on project objectives, deliverables, requirements, and status updates.

REQUIREMENTS 

• Bachelor’s degree with a concentration in Computer Science or related discipline.


• 3+ years of experience in financial services, asset management, or hedge funds with a focus on data operation and reference data support.


• Expertise in reference data content from common financial service data providers such as Bloomberg, Refinitiv, Barra, FactSet, etc.


• Experience troubleshooting and resolving data issues, along with experience in engaging with data consumers.


• Programming skills in SQL and Python.


• Experience working with large data sets.


• Strong oral and written communication skills.


• Strong analytical and problem-solving skills, with a keen attention to detail.


• Commitment to the highest ethical standards.