Big Data Engineer
Posted 6 Jul 2026
Advertised as “Big Data Engineer, Web3”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Visa sponsorship
- Not offered
No salary listed for this role.
_OKX will be prioritising applicants who have a current right to work in Singapore, 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 Opportunity
We are building the foundational data infrastructure that powers one of the world's leading crypto exchanges. As a Big Data Platform Engineer, you will design, build, and evolve the core platform services that enable hundreds of data engineers and analysts to move fast and ship reliable pipelines. You will be at the forefront of our AI agent initiative — embedding LLM-driven capabilities directly into the platform layer so that scheduling, cost optimization, and incident response increasingly run autonomously.
What You’ll Be Doing
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Platform Core: Design and operate large-scale distributed data systems
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Own the big data compute and storage infrastructure (MaxCompute/ODPS, Hologres, Spark)
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Build and maintain multi-site task orchestration that dynamically selects engines and enforces policy
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Drive reliability and performance improvements across batch and real-time pipelines
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AI Integration: Build the AI-native platform layer
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Develop and expose MCP (Model Context Protocol) tool interfaces so AI agents can interact with platform APIs
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Build the scheduling and cost-optimization agents that auto-tune resource allocation and alert severity
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Instrument platform telemetry to feed AI-driven SLA monitoring and anomaly detection