Data Client Support Analyst
The Role
As part of the broader Addepar Data Management & Quality organization, the Data Client Support team is responsible for ensuring our customers receive first-class support experience and are able get the most value out of their data in Addepar. The Data Client Support team sits at the nexus of Addepar's activity and requires daily communications with clients, data providers, and other personnel across the company.
Important: This role requires for you to be in office 3 days per week in Pune.
Applicants must have legal authorization to work in the country where this role is based on the first day of employment. Visa sponsorship is not available for this position.
What You’ll Do
Address client inquiries related to Addepar’s portfolio data feeds and on general data product functionalities within established SLAs.
Manage and complete requests from internal Data teams that require client outreach and/or action to resolve data verification issues.
Investigate client reported bugs and data processing issues, and triage with Engineering & Product when needed.
Partner with Account Managers, Solutions Architects, and other members of GTM to drive alignment on data related escalations and ensure positive outcomes for clients.
Participate in multi-org projects to improve internal processes and in broad company initiatives to stand up new product functionality or services.
Who You Are
2+ years of work experience in the financial investment/advisory industry with solid understanding of capital markets, portfolio accounting and customer relationship management.
Outstanding communication and interpersonal skills with excellent attention to detail.
Track record of demonstrating self-motivation to take on responsibility, a strong team-player mentality, and passion for delivering client-centric outcomes.
Proactive and diligent in documenting team processes and finding opportunities to team optimize workflow efficiencies
Willing to work flexible hours.
Familiarity with using MS Suite, Salesforce, Atlassian, SQL, Bloomberg, Thomson Reuters, or other common financial services systems and applications is preferable.