3 days ago
Hong Kong (city not specified)Staff+
Responsibilities
- Provide technical leadership for end-to-end batch and near-real-time ETL/ELT pipelines across raw, curated, semantic, and data product layers.
- Design and maintain logical and physical data models, data entities, relationships, metrics, and analytics-ready data products.
- Establish ingestion, orchestration, incremental and CDC loading, schema evolution, data quality, error handling, and performance-tuning practices.
- Partner with business, analytics, AI, architecture, governance, risk, security, and delivery teams to define trusted and discoverable data products.
- Apply CI/CD and DevOps practices to data pipelines, schemas, and products, and coordinate production handover and rollout stability.
- Align Asia ingestion pipelines with the global ingestion framework and coordinate requirements and framework releases with North America teams.
- Provide technical direction to delivery squads in Manila and Chengdu and align solution design, sequencing, and dependencies.
- Ensure compliance with data privacy, data residency, access control, audit, metadata, lineage, ownership, and quality requirements.
- Lead design and code reviews, mentor engineers, and contribute to enterprise data modelling, engineering, and product delivery standards.
Requirements
- Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
- 8+ years of data engineering experience, including hands-on logical and physical data modelling and technical leadership.
- Proven experience delivering production ETL/ELT pipelines across raw, curated, semantic, and data product layers.
- Strong hands-on experience with Azure Databricks, PySpark/Spark, Delta Lake, Unity Catalog, Azure Data Factory, Azure Data Lake Storage, advanced SQL, and Python.
- Experience with orchestration, incremental and CDC loads, data quality framework integration, and Spark performance tuning.
- Hands-on experience with near-real-time pipelines and streaming technologies such as Spark Structured Streaming, Azure Event Hubs, or Kafka.
- Understanding of data warehousing, dimensional modelling, analytics design patterns, and governed data-product approaches.
- Experience using ERwin, PowerDesigner, or an equivalent data modelling tool.
- Experience implementing CI/CD and DevOps for data platforms, including release readiness, performance testing, and production support handover.
- Strong written and verbal English communication skills for technical and non-technical audiences.
- Preferred: wealth or asset management domain knowledge, metadata-driven or global ingestion framework experience, regulated financial services privacy and access-control knowledge, AI/ML or GenAI data preparation experience, Microsoft Fabric and Power BI exposure, and spoken Mandarin.
Benefits
- Flexible hybrid working arrangement.
- Opportunities to learn and grow and shape the desired career path.
- Support for employee well-being and inclusion in a global team.
- Equal opportunity employment and reasonable accommodation support during the application process.
Tech Stack
Categories
Data Engineering
About Manulife
Manulife is a Toronto‑headquartered public financial services company that provides life and health insurance, retirement plans, and wealth and asset management to individuals and institutions. It earns premiums and fee income from insurance, investment, and advisory products delivered across Canada, Asia, and Europe, and operates as John Hancock in the United States. Founded in 1887, the company is listed on the Toronto Stock Exchange.