Data Enablement Lead

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

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.

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.