Own the end-to-end technical design and delivery of AI-powered business analytics applications, enabling a complete analytical loop from conversational data querying to anomaly attribution, insight generation and decision recommendations.
Lead the design and implementation of the intelligent attribution engine, covering dimensional attribution (multi-dimensional drill-down with contribution calculation), factor attribution (metric calculation-tree decomposition) and correlation-based attribution, to automatically identify root causes of key metric fluctuations.
Design automated metric monitoring and anomaly detection mechanisms, enabling AI to proactively surface issues and push attribution findings to stakeholders.
Lead the build-out of the metrics semantic layer, defining machine-readable business definitions, calculation logic, analysable dimensions and synonyms, so that natural-language intent is accurately translated into structured queries.
Package data and analytical capabilities into standardised tool interfaces (Function Calling / MCP protocol) for consumption by AI applications.
Build and maintain business analytics data models, taking ownership of dimensional modelling, metric processing and data quality assurance.
Establish evaluation and observability frameworks to quantify query accuracy, metric-definition correctness and reliability of attribution conclusions, driving a closed-loop bad-case optimisation process.
Implement controls over AI data access, including permission boundaries, data masking, data lineage, result explainability and full audit trails, in line with Hong Kong licensed corporation regulatory requirements.
Define data and AI technical standards, mentor junior team members, and drive cross-departmental alignment on metric definitions.
Requirements
Bachelor’s degree or above in Computer Science, Data, Statistics or a related discipline; minimum 8 years of experience in data engineering or data intelligence, including at least 2 years leading projects independently.
Expert-level SQL (complex queries, window functions, performance tuning) and strong proficiency in Python.
Deep expertise in dimensional modelling and data warehouse layering (ODS/DWD/DWS/ADS), with proven experience independently leading a complete data domain or data platform build.
Solid LLM application engineering capability, with in-depth understanding of RAG, Function Calling / Tool Calling and Text-to-SQL — including their implementation approaches, capability boundaries and failure modes — and the ability to constrain probabilistic generation with deterministic rules.
Hands-on experience with at least one data warehouse or compute engine (Hive, MaxCompute, Greenplum, ClickHouse, Databricks, etc.) and orchestration.
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Lead the design and implementation of the intelligent attribution engine, covering dimensional attribution (multi-dimensional drill-down with contribution calculation), factor attribution (metric calculation-tree decomposition) and correlation-based attribution, to automatically identify root causes of key metric fluctuations.
Design automated metric monitoring and anomaly detection mechanisms, enabling AI to proactively surface issues and push attribution findings to stakeholders.
Lead the build-out of the metrics semantic layer, defining machine-readable business definitions, calculation logic, analysable dimensions and synonyms, so that natural-language intent is accurately translated into structured queries.
Package data and analytical capabilities into standardised tool interfaces (Function Calling / MCP protocol) for consumption by AI applications.
Build and maintain business analytics data models, taking ownership of dimensional modelling, metric processing and data quality assurance.
Establish evaluation and observability frameworks to quantify query accuracy, metric-definition correctness and reliability of attribution conclusions, driving a closed-loop bad-case optimisation process.
Implement controls over AI data access, including permission boundaries, data masking, data lineage, result explainability and full audit trails, in line with Hong Kong licensed corporation regulatory requirements.
Define data and AI technical standards, mentor junior team members, and drive cross-departmental alignment on metric definitions.
Requirements
Bachelor’s degree or above in Computer Science, Data, Statistics or a related discipline; minimum 8 years of experience in data engineering or data intelligence, including at least 2 years leading projects independently.
Expert-level SQL (complex queries, window functions, performance tuning) and strong proficiency in Python.
Deep expertise in dimensional modelling and data warehouse layering (ODS/DWD/DWS/ADS), with proven experience independently leading a complete data domain or data platform build.
Solid LLM application engineering capability, with in-depth understanding of RAG, Function Calling / Tool Calling and Text-to-SQL — including their implementation approaches, capability boundaries and failure modes — and the ability to constrain probabilistic generation with deterministic rules.
Hands-on experience with at least one data warehouse or compute engine (Hive, MaxCompute, Greenplum, ClickHouse, Databricks, etc.) and orchestration.
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