Senior Data Analyst

1. Role Summary

We are seeking a Senior Data Analyst with a strong data warehouse modelling experience, and a Data Architect mindset to bridge the gap between complex business requirements and high-performance data engineering. Unlike a traditional analyst who reacts to tickets, you will leverage deep data modelling experience to proactively lead the design of the data warehouse and data lake layers with a design-first approach.

2. Key Responsibilities

• Enterprise Blueprinting: Lead the logical and physical design of the data warehouse DWD/DWS/ADS layers, moving away from trial-and-error and reactive ticket based modeling to create a unified, high-integrity data environment.

• Proactive Requirement Engineering: Anticipate data requirements through deep know-how in data model to satisfy business growth analysis, data driven near-real-time recommendations, and financial analysis/reporting needs, ensuring majority of warehouse data is already captured and structured before the business requests it.

• Architectural Oversight: Serve as the bridge to the Data Engineering team, ensuring that data logic migrated or extended from Redshift to EMR (Spark/Hive) maintains business accuracy and performance standards.

• Serve as the final gatekeeper of data accuracy.

• Regulatory & Risk Data Modeling: Design data flows that meet regulatory standards for data aggregation and risk reporting. Ensure Critical Data Elements (CDEs) have clear lineage and robust quality checks.

• Financial Logic Implementation: Build the foundational data warehouse model and business logic combining all SORs for complex KPIs such as Assets Under Management (AUM), accounting reconciliations, and risk metrics.

• Global Standardization: Lead global expansion data modelling; partner with cross-functional business and operations teams to align data definitions across global regions, ensuring a single source of truth for all decision-making.

• Understand or willing to self-teach the use cases and data model supporting data driven products and business growth, such as real time and batch recommendation/up selling/cross selling, etc.

C. Data Project Execution:

• Stakeholder and executive communication as data team representative

• As product owner, own Data Project's planning, data model design, monitoring, data integrity testing and project communication. Data Warehouse buildup, data integration and migration, BI buildup, etc, are all deemed Data Projects.

D. Technical Validation & Optimization

Since you will act as the Technical Product Owner for the data domain. You are expected to speak the language of Data Engineering to co-design scalable models, but your primary focus is the integrity and business-readiness of the data layer, not the day-to-day management of infrastructure pipelines. Leveraging below technique, you will define and validate the complex data warehouse business logic.

• High-Performance SQL: Independently develop and optimize complex SQL

• Decision-Support Visuals: Design executive-level dashboards in Power BI (DAX) or AWS QuickSight, focusing on data storytelling and intuitive navigation for complex financial data.

3. Requirements

• Bilingual Proficiency: Excellent command of spoken English and Chinese. Ability to lead meetings in English and Chinese, and draft high-quality technical documentation.

• Industry Background: Extensive experience in Financial Service data modelling.

• Deep Know-How: Familiarity with business metrics such as customer experience, accounting, regulatory or risk, etc.

• Visionary Approach: Proven ability to design data models based on industry best practices rather than piecemeal user requests.

• Core Analytics: Advanced SQL, Power BI (DAX), and AWS QuickSight.

• Data Engineering Literacy: Strong familiarity with or willingness to learn AWS EMR (Spark/Hive) and Airflow orchestration.

• Data Modeling: Expert in Dimensional Modeling (Star/Snowflake schemas) and 3rd Normal Form (3NF).

• Technical Agility: Proficiency in Python for data manipulation is a significant plus.