Data Engineering Director

OKX will be prioritising applicants who have a current right to work in Hong Kong, and do not require OKX's sponsorship of a visa.  

Who We Are:

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.

About the team:

The OKX Data Team owns and powers the company’s enterprise-wide data ecosystem. We are responsible for the full data lifecycle — from technology selection and architecture design to data ingestion, storage, ETL development, visualization, business intelligence, and advanced data science.

Our team drives end-to-end data ownership across OKX, ensuring that data is reliable, scalable, and actionable. We build the foundational data infrastructure that empowers evidence-based decision-making and continuously enhances the quality of our products and services.

As a Data Engineering Director, you will lead a high-impact team in designing and scaling mission-critical data platforms that serve the entire organization. You will also play a key role in driving the adoption of Agentic AI to elevate how we build, operate, and evolve our Data Engineering capabilities.

Responsibilities:

• Lead and mentor a team of Data Engineers to design and build resilient, scalable, and high-performance data pipelines for both batch and real-time streaming workloads
• Championing Agentic AI adoption across workflows (text-to-SQL agents, automated data quality remediation, self-healing pipelines etc.)
• Defining best practices, guardrails, and evaluation frameworks for safely deploying AI agents in the data lifecycle
• Architect and implement robust Data Warehouse and Data Mart solutions, including data modeling and ETL pipeline development
• Design, build, and maintain analytics-ready Data Marts using best-practice dimensional modeling techniques (star and snowflake schemas)