Description We are hiring for a detail-oriented Data Steward to support APAC Operations in processing and managing bordereaux data. This role ensures data quality and integrity across underwriting, actuarial, and finance functions, enabling accurate and timely data that underpins key business decisions. The role offers hands-on experience in insurance data analytics and the opportunity to grow with our evolving data architecture and strategic goals. Responsibilities Main Responsibilities: Bordereaux Ingestion and Management Track bordereaux files from Brokers, Coverholders, and third parties. Validate file formats and ensure completeness prior to processing, with a strong emphasis on data quality Process bordereaux files to ensure complete and accurate data capture, validation and quality checks across internal systems, including using excel and Databricks Clean, map, organise and maintain data used by our underwriting, claims and finance teams, primarily across the delegated authority distribution channel. Collaborate with other data analysts and system architects to refine mapping rules and assist with system reconciliation. Support preparation of regular reports and dashboards. Stakeholder Collaboration Work closely with underwriting, claims, actuarial, and finance teams to understand data requirements. Identify and communicate data issues and remediation plans clearly and promptly. Continuous Improvement Contribute to the enhancement of bordereaux processing tools and workflows. Identify opportunities for automation and efficiency gains. Stay current with market standards (e.g. Lloyd’s Core Data Record, ACORD) and internal data governance policies. Qualifications Skills and Experience Bachelor’s degree in Data Analytics, Actuarial Studies, Business, Information Systems, or a related field. Strong Excel and Power Query skills Strong Python programming skills Familiarity with PowerBI and Databricks. Experience with SQL or other coding languages for data analysis Data governance, management and personal data protection experience preferable Strong analytical mindset and attention to detail Enthusiasm for problem solving and curiosity to learn Solid foundation and understanding in data cleaning and transformation. Good communication skills and proactive attitude.
