# Senior AI Analytics Engineer at Crypto.com

> Hybrid · Hong Kong · Senior · Data engineering · open. Source: https://alderhq.app/jobs/lever%3Acrypto%3A6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c (read 2026-11-05).

## Facts (observed)

- Company: [Crypto.com](https://alderhq.app/companies/crypto-com)
- Location text: Hong Kong
- Where it can be done from (inferred): Hybrid · Hong Kong (evidence: "Location: Hong Kong")
- Posted: 2026-10-22 (14 days ago)
- First seen by alder: 2026-11-03 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.lever.co/crypto/6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c
- Skills listed (inferred): Python, Airflow, Snowflake, dbt, SQL, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### 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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/lever%3Acrypto%3A6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.