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Crypto.com

Senior AI Analytics Engineer

Crypto.com Hong Kong, China

Financial Services · 1,001-5,000 employees

Oct 22
data-analyst Senior (5-10 yrs) Full-time China
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About the role

Design and maintain production-grade data models, scalable transformation pipelines, and semantic layers, using AI tools to support documentation, coding, and pipeline troubleshooting. Establish data quality and governance practices, optimize performance and costs, and occasionally translate stakeholder needs into technical specifications and data requirements.

What they look for

Advanced SQL Python Data Modeling Analytics Engineering Data Engineering LLM Operations AI Agentic Workflows Feature Engineering dbt Airflow Prefect Dagster Snowflake Databricks BigQuery Data Quality Testing

Requirements

Requires 5+ years of experience in data engineering, analytics engineering, or data science, including at least 2 years in an AI environment. Candidates should have advanced SQL skills, experience with modern data platforms and orchestration tools, production AI agentic workflow experience, and strong software engineering and business analysis practices.

Full 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.

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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.

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