Data Enablement Lead

Lalamove; Hong Kong; On-site; HKD 900,000 - 1,300,000; Full time

Job summary

Lalamove in Hong Kong seeks a Data Enablement Lead to bridge backend data infrastructure with business analytics. You will own the BU's data stack, collaborate with the central team, and build localized pipelines for speed and agility.

You will design high-performance data cubes and AI-driven data bots, enabling non-technical stakeholders to query data easily and get instant insights. This role emphasizes governance and scalable data tooling.

Qualifications

• 5+ years of experience in data engineering, analytics engineering, or technical data product management.
• Proficiency in SQL and Python with strong experience in large-scale data warehouses (Hive) and modern OLAP engines (Doris, ClickHouse, StarRocks).
• Experience building interactive data tools (e.g., Cube.js, semantic layers) or leveraging AI/LLM frameworks to simplify data retrieval.
• Deep understanding of dimensional modeling, star schemas, data aggregation, and query optimization.

Responsibilities

• Build, maintain, and optimize data pipelines feeding the BU analytics layer.
• Architect non-data-person-facing tools to automate data access (data cubes, semantic layers, AI data bots).
• Establish best practices for data modeling, pipeline monitoring, and data quality in the BU repository.
• Coordinate as the technical interface between analytics teams and central platform data engineering.
• Scope, prioritize, and manage end-to-end analytics engineering projects and enable self-serve tools for stakeholders.

Skills

SQL

Python

Data Warehousing

Hive

Doris

ClickHouse

StarRocks

Data Modeling

AI Tools

Cube.js

Tools

Cube.js

LangChain

OpenAI APIs

Text-to-SQL

Job description

We are seeking a versatile, highly motivated Data Enablement Lead to bridge the gap between complex backend data infrastructure and business-facing analytics solutions.

In this role, you will be the core engine driving analytics, reporting, and self-service data products for our Business Unit (BU). While our central data infrastructure team handles enterprise-wide platform needs, you will take ownership of our BU's specific data stack—coordinating with the central team to manage existing upstream pipelines while building and maintaining our own localized data pipelines when speed and agility are required.

Beyond standard dashboards and data pulls, your goal is to revolutionize how non-technical stakeholders interact with data. You will design next-generation data consumption tools—ranging from high-performance data cubes to AI-driven query bots—empowering our BU to get answers instantly.

What we seek:

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).
• Architecture & Standards: Establish best practices for data modeling, pipeline monitoring, and data quality within our BU's local repository

Technical Project Management & Coordination

• Cross-Team Collaboration: Act as the primary technical interface between analytics / operational team and the central platform data engineering team.
• Project Delivery: Scope, prioritize, and manage the end-to-end lifecycle of analytics engineering projects, translating non-technical needs into clear technical specifications.
• Enablement & Stakeholder Management: Educate and support operational team on self-serve tools, documentation, and data literacy initiatives.

What you'll need:

Technical Skills

• 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 Modeling: Deep understanding of dimensional modeling, star schemas, data aggregation, and query optimization techniques.

Project & Stakeholder Management

• Proven ability to coordinate across cross-functional engineering teams with competing business priorities.
• Strong project management skills—able to track dependencies, mitigate risks, and manage stakeholder expectations clearly without micro-managing.
• Pragmatic approach to the "Build vs. Coordinate" tradeoff—knowing when to rely on central platforms versus when to build lightweight local solutions.

To all candidates- Lalamove respects your privacy and is committed to protecting your personal data.

This Notice will inform you how we will use your personal data, explain your privacy rights and the protection you have by the law when you apply to join us. Please take time to read and understand this Notice. Candidate Privacy Notice: https://www.lalamove.com/en-hk/candidate-privacy-notice