Job Title: Reference Data Operations Analyst / Reference Data Analyst
Comp: 150-500k total compensation (candidate dependent)
Ideal YOE: 3-15 years
Summary: A successful hedge fund is looking to bring on a qualified candidate in the reference data operations or data analysis space. A qualified candidate will have experience with reference and market data as well as SQL/Python.
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.
- Discover, source, validate, and drive the on-boarding of new data-sets that enable unique investment opportunities, working across internal teams and external vendors
- Create and maintain internal data library that collates information internally and externally on an ongoing basis
- Work with technologists, project managers, and business analysts to deliver reference data system upgrades
- Develop and document standardized processes for data support, monitoring, and quality assurance.
- Collaborate with global team members to ensure seamless transitions between support regions.
- Follow and maintain a daily playbook of operational procedures and controls
- Capture all metadata feeding into an Incident Management program for all reference data issues
- Produce operational reports for team and management reviews on a regular basis
Qualifications
- 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 analytical and problem-solving skills, with a keen attention to detail.
