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Open nowPosted 14 hours ago

Senior AI Analytics Engineer

Crypto90 open roles

Where
Hong Kong
Work mode
Hybrid
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Your applicationOpen nowSenior AI Analytics EngineerCrypto · Hong Kong

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. Crypto postings stay open a median of 8 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 14 hours ago

Crypto median: 8 days open

The posting

About the Role

As a Senior AI Analytics Engineer in our Data Team, you will bridge the gap between Data Engineering, Tech, and Product. You will play a pivotal role in operationalizing AI/LLM models, building scalable data transformation pipelines, standardizing data models, and establishing best practices.

In addition to core analytics engineering, you will occasionally act as a technical Business Analyst on select data initiatives—partnering with business teams to translate ambiguous operational needs into clear technical specs and data requirements.

Key Responsibilities

  • Data & AI Architecture: Design, build, and maintain production-grade data models to bridge raw data streams with downstream applications and BI tools. Using LLMs and AI tools to generate data documentation, write SQL/Python transformations, convert warehouse dialects, and diagnose pipeline error logs.
  • Analytics Engineering: Standardize data modeling practices and build robust, reliable, scalable semantic layers that feed clean, trustworthy data to downstream analytics and reporting.
  • Business & Requirements Analysis: Occasionally lead scoping discussions with product, tech and finance stakeholders to translate business requirements into functional specs, user stories, and data mapping documents.
  • Business Logic Translation: Reconciling data definitions across departments, a critical human-led task that AI cannot fully automate
  • Data Quality & Governance: Establish automated testing, monitoring, and data quality frameworks to guarantee financial-grade reliability.
  • Performance Optimization: Optimize query performance, warehouse costs, and pipeline orchestration.

Requirements & Qualifications

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