Broker Operations Data Quality Analyst

4 weeks ago

Madison, United States York Solutions, LLC Full time

Description

• Type: Contract-to-Hire, after 6 months • Location: Remote for anyone living in: AR, AZ, DE, FL, GA, IA, IL, KS, KY, MD, ME, MI, MN, MO, ND, NE, OK, SD, TN, TX, VA, WI.

Position Overview: The Broker Operations Data Quality Analyst will play a pivotal role in ensuring accuracy, quality, and compliance of broker data. Leveraging strong analytical skills and attention to detail, this role will work closely with various teams to analyze data, identify discrepancies, and contribute to maintaining a high standard of data integrity in alignment with industry regulations and internal policies.

Position Overview: The ideal candidate has experience with analyzing data and providing reporting, compliance and regulatory monitoring, and audit skills. They would also have a minimum of 3 years of recent experience executing commission payments

Responsibilities: Data Quality Analysis, Validation and Cleansing (30%) • Conduct thorough analysis of brokerage data to identify inconsistencies, errors, and discrepancies in compliance-related information. • Collaborate with relevant departments to rectify data inaccuracies and Monitor data to ensure compliance with industry regulations, internal policies, and legal standards. • Validate data accuracy by comparing and cross-referencing data from various sources. • Execute data cleansing procedures to eliminate redundancies, inaccuracies, and outdated information. Compliance Monitoring and Cross-Functional Collaboration (20%) • Monitor data to ensure compliance with industry regulations, internal policies, and legal standards. • Collaborate with compliance teams to address potential compliance issues and rectify deviations. • Collaborate with compliance, IT, and other relevant teams to enhance data quality processes and address compliance-related concerns. Reporting and Documentation (30%) • Generate regular and ad-hoc reports on data quality, compliance performance, and identified issues. • Maintain detailed documentation of data validation processes and findings. Process Improvement, Training and Support (20%) • Continuously assess data quality processes, identifying areas for improvement, automation and efficiency enhancements. • Propose and implement enhancements to ensure robust data quality practices. • Provide training and guidance to team members and end-users on data quality best practices. • Promote a culture of data accuracy and compliance awareness.

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