Hong Kong SAR
Open 3 months, first seen by us 104 days agoWork setup in the employer’s posting: onsiteFrom the employer’s posting
Employment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Build, maintain and optimize data pipelines feeding the BU analytics layer
Design data cubes, semantic layers and AI/LLM-powered query bots for non-technical users
Set best practices for data modeling, pipeline monitoring and data quality
Act as the technical interface between analytics teams and central platform engineering
Scope, prioritize and manage end-to-end analytics engineering projects
Build, maintain, and optimize data pipelines feeding our BU’s analytics layer.
setting up data cubes/semantic layers and building AI/LLM-powered data bots
Establish best practices for data modeling, pipeline monitoring, and data quality within our BU's local repository
Act as the primary technical interface between analytics / operational team and the central platform data engineering team.
Scope, prioritize, and manage the end-to-end lifecycle of analytics engineering projects
Stack Expertise: Strong proficiency in SQL and Python. Solid experience with large-scale data warehouses (Apache Hive) and modern OLAP engines (Apache Doris, ClickHouse, StarRocks, or similar).
Proven ability to coordinate across cross-functional engineering teams with competing business priorities.
Data Modeling: Deep understanding of dimensional modeling, star schemas, data aggregation, and query optimization techniques.
Open 3 monthsWe first saw this posting 104 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 104
Up longer than most. Similar jobs usually come down between day 7 and day 54.
Well past Lalamove’s usual: its jobs come down by about day 45.
From 7,965 similar postings that came down, and 8 of Lalamove’s. Coming down is not always a hire.
13 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Data Engineering & Warehousing: 5+ years of experience in data engineering, analytics engineering, or technical data product management. - Stack Expertise: Strong proficiency in SQL and Python. Solid experience with large-scale data warehouses (Apache Hive) and modern OLAP engines (Apache Doris, ClickHouse, StarRocks, or similar). - Data Product & AI Innovation: Demonstrated interest or experience in building interactive data tools (e.g., Cube.js, semantic layers) or leveraging AI/LLM frameworks (e.g., LangChain, OpenAI APIs, Text-to-SQL pipelines) to simplify data retrieval.
- Data Pipelines & Backend: Build, maintain, and optimize data pipelines feeding our BU’s analytics layer. Work across our core data platform (Apache Hive) and high-performance OLAP backend (Apache Doris). - Next-Gen Data Tools: Architect non-data-person-facing tools to automate data access, such as setting up data cubes/semantic layers and building AI/LLM-powered data bots (e.g., text-to-SQL / natural language data querying). - Architecture & Standards: Establish best practices for data modeling, pipeline monitoring, and data quality within our BU's local repository
- Stack Expertise: Strong proficiency in SQL and Python. Solid experience with large-scale data warehouses (Apache Hive) and modern OLAP engines (Apache Doris, ClickHouse, StarRocks, or similar). - Data Product & AI Innovation: Demonstrated interest or experience in building interactive data tools (e.g., Cube.js, semantic layers) or leveraging AI/LLM frameworks (e.g., LangChain, OpenAI APIs, Text-to-SQL pipelines) to simplify data retrieval. - Data Modeling: Deep understanding of dimensional modeling, star schemas, data aggregation, and query optimization techniques.
Hands-On Engineering & Tooling - Data Pipelines & Backend: Build, maintain, and optimize data pipelines feeding our BU’s analytics layer. Work across our core data platform (Apache Hive) and high-performance OLAP backend (Apache Doris). - Next-Gen Data Tools: Architect non-data-person-facing tools to automate data access, such as setting up data cubes/semantic layers and building AI/LLM-powered data bots (e.g., text-to-SQL / natural language data querying).
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New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Full-time
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Hong Kong SAR
Working pattern and location restrictions need checking in the full posting.
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