Senior Data Analyst - Data Warehouse Modelling

About the role

A Senior Data Analyst with strong data warehouse modelling experience and a Data Architect mindset is sought to bridge complex business requirements with high-performance data engineering. The role takes a proactive, design-first approach: instead of reacting to tickets, it leads the design of the data warehouse and data lake layers so that most data is captured and structured before business requests it.

Key responsibilities

• Lead the logical and physical design of the data warehouse DWD/DWS/ADS layers, moving away from trial-and-error and reactive modelling toward a unified, high-integrity data environment.
• Anticipate data requirements for business growth analysis, near-real-time recommendations and financial analysis and reporting.
• Act as the bridge to the Data Engineering team, ensuring data migrated or extended from Redshift to EMR (Spark/Hive) keeps business accuracy and performance standards.
• Design data flows that meet regulatory standards for aggregation and risk reporting, with clear Critical Data Element (CDE) lineage and quality checks.
• Build the foundational warehouse model and business logic for complex KPIs such as Assets Under Management (AUM), accounting reconciliations and risk metrics.
• Lead global expansion data modelling and align data definitions across regions with business and operations teams.
• Own each data project as product owner: planning, model design, monitoring, integrity testing, communication, data warehouse build-up, integration, migration and BI build-up.
• Develop and optimise complex SQL, and design executive dashboards in Power BI (DAX) or AWS QuickSight.

Required profile

• Bilingual: excellent spoken English and Chinese, able to lead meetings and write technical documentation in both languages.
• Extensive experience in financial services data modelling, with knowledge of customer experience, accounting, regulatory and risk metrics.
• Proven ability to design data models from industry best practices rather than piecemeal requests.
• Willingness to understand or self-teach data models behind real-time and batch recommendation, up-selling and cross-selling use cases.

Required skills

• Advanced SQL, Power BI (DAX) and AWS QuickSight.
• Dimensional modelling (star/snowflake schemas) and 3NF.
• Familiarity with AWS EMR (Spark/Hive) and Airflow, or willingness to learn them.
• Python for data manipulation is a significant plus.