Manager, Data Analytics

Lead data analytics initiatives within HSBC Retail Banking & Wealth, overseeing AI-driven data model design, DataOps transformation, and cross-market data design discussions.

Kowloon City, Kowloon City, Hong Kong
Full-time
Posted Nov 4, 2026

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Responsibilities

Serve as the go-to person for banking data requests
Lead AI-driven and analytics-driven data model design
Manage end-to-end data transformation program
Lead DataOps transformation for self-serve ETL
Build and maintain data pipelines and quality controls
Coordinate cross-market data design discussions
Support data analysis and business insights generation

Requirements

Experience in a banking

Skills

SQL Python PostgreSQL Airflow BigQuery Spark SQL Python Java JavaScriptVisual Studio CodePyCharmIntelliJApache Spark Apache Airflow Jenkins Terraform BigQuery PostgreSQLRedshiftSnowflakeGitHub CopilotHSBC Productivity Suite

Full description

We are currently seeking a high calibre professional to join our team as a Manager, Data Analytics. In this role you will: Serve as the “go-to” person within the Data & Analytics organization in HSBC Retail Banking & Wealth for banking data requests from business stakeholders. Lead AI-driven and analytics-driven data model design, and manage the end-to-end data transformation program in partnership with multiple teams. Lead the DataOps transformation to enable self-serve, high-performant data ETL for both ad-hoc analytical purposes and real-time large Machine Learning / AI model inference. Build and maintain data pipelines (ETL/ELT), implement data quality controls, and perform peer code reviews. Coordinate and drive cross-market and cross-entity data design discussions to maximize reuse and data synergies. Lead and/or support data analysis and business insights generation. To be successful you will need: 5+ years’ experience in data-related roles within a financial institution (banking experience strongly preferred). Strong proficiency in SQL, Python, PostgreSQL, and cloud-native data pipeline automation tools (e.g., Airflow, BigQuery). Spark experience is a plus. Demonstrated experience delivering large-scale data projects or data products using modern engineering practices and tooling (e.g., data pipelines and/or CI/CD workflows). Experience designing and developing high-performance solutions for extremely large-scale data processing, storage, and wrangling, ideally including real-time workloads. Solid understanding of DevOps, DataOps, and/or MLOps principles. Cloud-native data ETL development is preferred Experience handling unstructured data is an advantage. Technology Stack Technology stack provisioned in the team including: Languages: SQL, Python, Java, JavaScript IDE: Visual Studio Code, PyCharm, IntelliJ Platforms and Services: Managed Service for Apache Spark, Apache Airflow CI/CD: Jenkins, Terraform Data warehouse: BigQuery, PostgreSQL, Redshift, Snowflake AI: GitHub Copilot, HSBC Productivity Suite