Lalamove

On-demand last-mile delivery platform

Data Enablement Lead

Data Engineering +1Confirmed live in the last 24 hours

Full-timeHong KongNo salary listedIn-person

About the Job

Requirements

At least 5 years of experience in data engineering, analytics engineering, or technical data product management.
Strong proficiency in SQL and Python.
Solid experience with large-scale data warehouses such as Apache Hive and modern OLAP engines such as Apache Doris, ClickHouse, StarRocks, or similar.
Demonstrated interest or experience in building interactive data tools such as Cube.js or semantic layers, or leveraging AI/large language model frameworks such as LangChain, OpenAI APIs, or text-to-SQL pipelines to simplify data retrieval.
Deep understanding of dimensional modeling, star schemas, data aggregation, and query optimization techniques.
Ability to coordinate across cross-functional engineering teams with competing business priorities.
Project management skills to track dependencies, mitigate risks, and manage stakeholder expectations without micromanaging.
Pragmatic judgment about when to rely on central platforms versus building lightweight local solutions.

Responsibilities

Build, maintain, and optimize data pipelines feeding the Business Unit's analytics layer, working across Apache Hive and Apache Doris.
Architect tools to automate data access, including data cubes or semantic layers and AI/large language model-powered data bots such as text-to-SQL or natural-language data querying.
Establish best practices for data modeling, pipeline monitoring, and data quality within the Business Unit's local repository.
Act as the primary technical interface between the analytics or operational team and the central platform data engineering team.
Scope, prioritize, and manage the end-to-end lifecycle of analytics engineering projects, translating non-technical needs into clear technical specifications.
Educate and support the operational team on self-service tools, documentation, and data literacy initiatives.

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

About the Company

Lalamove

On-demand last-mile delivery platform

Lalamove is a technology-driven logistics platform that provides on-demand last-mile delivery in urban areas across Asia and the Americas. Its mobile app connects individuals and businesses with a network of drivers to handle fast or scheduled deliveries, ranging from small parcels to bulky items. Deliveries are priced based on distance, weight, and optional services like multiple stops. The service targets a wide range of customers, from individuals to small businesses and large enterprises, and aims to offer a seamless, efficient delivery experience by leveraging its driver network and digital platform.

Company size: 5,001-10,000
Company stage: Series F
Total funding: $2.5B
Headquarters: Sham Shui Po District, Hong Kong
Founded: 2013

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