Senior AI Analytics Engineer

Crypto.com; Hong Kong; On-site; HKD 900,000 - 1,500,000; Full time

Job summary

Crypto.com is seeking a Senior AI Analytics Engineer to bridge Data Engineering, Tech, and Product. You will operationalize AI/LLM models, build scalable data pipelines, and standardize data models.

You will also act as a technical Business Analyst, translating ambiguous needs into specs, data requirements, and measurable success metrics. This role emphasizes data quality, governance, and cost-efficient analytics while collaborating across teams in a dynamic environment.

Qualifications

• 5+ years in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment.
• 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).
• Understanding of feature engineering, AI agentic workflows, and evaluation metrics.
• Experience deploying AI agentic workflows in production environments.
• Business Analysis Skills: gather business requirements, define metrics, translate needs into data assets.
• Software Engineering Principles: Git, CI/CD for data pipelines, production code testing.

Responsibilities

• Data & AI Architecture: Design, build, and maintain production-grade data models bridging raw data streams with downstream apps and BI tools.
• Analytics Engineering: Standardize data modeling practices and build robust semantic layers for trustworthy data.
• Business & Requirements Analysis: Lead scoping discussions to translate business needs into functional specs and data mappings.
• Business Logic Translation: Reconcile data definitions across departments to ensure consistency.
• Data Quality & Governance: Establish automated testing, monitoring, and data quality frameworks for reliability.
• Performance Optimization: Improve query performance, warehouse costs, and pipeline orchestration.

Skills

Advanced SQL

Feature engineering

AI workflows design

Business analysis

Production deployment

Data modeling

Data governance

Tools

dbt

Airflow

Prefect

Dagster

Snowflake

Databricks

BigQuery

Git

CI/CD

Testing

Job 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.