# Senior AI Analytics Engineer at Crypto.com

Senior AI Analytics Engineer at Crypto.com in Hong Kong. Hybrid, full-time. Posted October 22, 2026.

- Company: Crypto.com
- Location: Hong Kong
- Workplace: Hybrid
- Job type: Full-time
- Experience: 5+ years
- Posted: 2026-10-22
- Apply: https://jobs.lever.co/crypto/6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c/apply
- Source: https://jobmagic.ai/jobs/senior-ai-analytics-engineer-cryptocom-v6q86g

## Requirements

- 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.

## Description

**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

• **Experience:** 5+ years of experience in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment.

• **Technical Mastery:**

Advanced **SQL** capabilities.

Hands-on expertise with modern data transformation and orchestration frameworks (**dbt**, **Airflow**, **Prefect**, **Dagster**).

Deep experience with cloud data warehouses (**Snowflake**, **Databricks**, **BigQuery**).

• **AI Technical Stack:**

Understanding of feature engineering, AI agentic workflow lifecycles, and evaluation metrics.

Experience with deploying AI agentic workflows in production environments.

• **Business Analysis Skills:** Demonstrated ability to occasionally gather business requirements, define key metrics, and translate stakeholder needs into structured data assets.

• **Software Engineering Principles:** Strong experience with version control (**Git**), CI/CD practices for data pipelines, and production code testing.