Data Enablement Lead

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.

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

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.

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.