1. Role Summary
We are seeking a Senior Data Analyst with a strong banking data warehouse modelling experience, and a Banking 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 banking data modelling experience to proactively lead the design of the data warehouse and data lake layers with a "design-first" approach.
You will be the Technical Product Owner for the banking data domain, and the primary consultant for the business, anticipating data needs for Regulatory Compliance from central bank (BCBS 239, Basel III alike), Business Growth, and Financial Analysis (Deposit, Cashflow, AUM, NIM, etc). You will ensure that our data models are not just built for today’s reports, but are scalable, performant, and logically sound for the future of the bank.
2. Key Responsibilities
A. Strategic Data Modeling & Design
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 banking data model to satisfy banking regulations, 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.
B. Regulatory, Financial and Business Growth Subject Matter Expertise
Compliance Architect: 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 (core banking, GL, etc) for complex KPIs such as Assets Under Management (AUM), accounting reconciliations, and risk metrics.
Global Standardization: Lead global expansion data modelling; partner with mid-and-back-office 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 retail/corporate banking 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 banking 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
Language & Communication
Bilingual Proficiency: Excellent command of spoken English and Chinese. Ability to lead meetings in English and Chinese, and draft high-quality technical documentation.
Banking Domain Expertise
Industry Background: Extensive experience in Banking or 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.
Technical Stack Mastery
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.