Data Enablement Lead

About the Position

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.

Responsibilities

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.

Requirements

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.

We Offer

• Competitive salary
• Opportunity to work in a dynamic fintech environment
• Collaborative and innovative team culture

About the Company

Lalamove is a leading tech company in Asia, providing a platform for on-demand transportation and logistics services. We are committed to revolutionizing the way people and goods move in urban environments.

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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.