Lead Data Engineer – Data Products

We are seeking a Lead Data Engineer – Data Products in Hong Kong to design and deliver trusted, reusable data products for Global Wealth & Asset Management (GWAM) Asia.

This is a hands-on technical leadership role: you will guide the data engineers building end-to-end ETL/ELT pipelines from raw, through curated, to semantic and data product layers, while owning the data models that underpin them.

You will play a key role in building GWAM Asia's regional data platform and the reusable data products that power analytics and AI across multiple Asia markets.

The ideal candidate combines strong data design and pipeline engineering capabilities with a product mindset, focused on delivering high-quality data products for business, analytics, and downstream technology consumers.

Responsibilities:

• Provide technical leadership in designing, building, and optimising end-to-end batch (and near-real-time where required) ETL/ELT pipelines across the raw, curated, and semantic/data product layers (medallion architecture) on Azure, using Azure Data Factory, Azure Databricks (PySpark, Delta Lake), Azure Data Lake Storage, and Azure SQL.
• Establish robust pipeline engineering practices, including ingestion patterns, orchestration, incremental and CDC loads, schema evolution, integration with the Data Quality (DQ) Framework, error handling, and performance tuning.
• Partner with business stakeholders, analytics, and AI teams to define data products that align with business domains, use cases, and outcomes, and ensure they are discoverable, trusted, and well understood.
• Translate business requirements into well-defined data entities, relationships, and metrics; contribute to conceptual data models with the Lead Data Solution Architect; and design and maintain the logical and physical data models that underpin scalable, reusable data products, using ERwin, PowerDesigner, or equivalent modelling tools to ensure consistency across data products.
• Engineer curated, analytics-ready data products optimised for BI, reporting, APIs, and semantic models (e.g., Power BI).
• Perform reverse engineering and model reconciliation to align existing datasets and pipelines with logical models and product intent.
• Apply CI/CD and DevOps practices to data pipelines, schemas, and data products using Azure DevOps, GitHub, or Jenkins, and work with the Production Support team to ensure smooth handover, stable pipeline performance, and issue-free rollout of new and changed pipelines.
• Align GWAM Asia ingestion and pipelines with the global ingestion framework, working with the North America global data platform team to raise Asia-specific requirements and roll out new framework versions.
• Provide technical direction to delivery squads in Manila and Chengdu, and work with the Lead Data Solution Architect to align solution design, sequencing, and dependencies.
• Ensure data products meet data privacy, data residency, access control, and audit requirements, in partnership with governance, risk, and security teams.
• Support the DQ team in integrating DQ rules from the DQ Framework into pipelines and resolving pipeline-related data issues.
• Support the Data Governance team in capturing and maintaining data product metadata, including business definitions, lineage, ownership, and quality expectations.
• Identify opportunities to standardise, reuse, and evolve data products across domains and initiatives.
• Lead design and code reviews, mentor engineers, and contribute to enterprise standards for data modelling, engineering, and data product delivery.

Required Qualifications:

• Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
• 8+ years of experience in data engineering, including hands-on logical and physical data modelling using ERwin, PowerDesigner, or an equivalent tool, and technical lead experience, leading design and code reviews and mentoring engineers, ideally within distributed or cross-location teams.
• Proven track record of leading the delivery of end-to-end ETL/ELT pipelines across raw, curated, and semantic/data product layers (e.g., medallion architecture) in production environments.
• Strong hands-on experience with Azure Databricks (PySpark/Spark, Delta Lake, Unity Catalog), Azure Data Factory, Azure Data Lake Storage, and advanced SQL and Python.
• Strong pipeline engineering skills, including orchestration, incremental and CDC loads, integration with data quality frameworks, and Spark performance tuning.
• Hands-on experience building near-real-time pipelines with streaming technologies (e.g., Spark Structured Streaming, Azure Event Hubs, Kafka).
• Solid understanding of data warehousing, dimensional modelling, and analytics design patterns.
• Hands-on experience with ERwin, PowerDesigner, or an equivalent data modelling tool.
• Proven experience delivering data using data-product-oriented approaches (curated layers, semantic models, governed datasets).
• Experience implementing CI/CD and DevOps for data platforms (e.g., Azure DevOps, GitHub, or Jenkins), including release readiness, performance testing, and handover to production support teams.
• Strong written and verbal communication skills in English, with the ability to clearly articulate data product design and usage to technical and non-technical audiences.

Benefits:

• We’ll empower you to learn and grow the career you want.
• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
• As part of our global team, we’ll support you in shaping the future you want to see.