lvl100Sign in

crypto

Senior AI Analytics Engineer

  • Hong Kong
  • Full-time
  • Posted

Skills

  • CI/CD
  • Git
  • Large Language Models
  • Python
  • Requirements Management
  • SQL
  • Snowflake
  • Test Automation

Job description

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.

Browse all jobs by role, city or company