{"active":true,"apply_url":"https://jobs.lever.co/crypto/6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c/apply","company":{"claimed":false,"name":"Crypto.com","slug":"crypto-com","website":"https://crypto.com"},"countries":["HK"],"employment_type":"full-time","first_seen_at":"2026-10-31T10:45:10Z","getwork_url":"https://getwork.dev/jobs/lv-crypto-com-6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c/","id":"lv-crypto-com-6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c","injection_flag":false,"last_seen_at":"2026-11-03T06:15:09Z","location":"Hong Kong","published_at":"2026-10-22T07:45:11Z","regions":["apac"],"remote":"hybrid","role_family":"ml","salary":null,"seniority":"senior","stack":["python","llm"],"stack_mentioned":[],"title":"Senior AI Analytics Engineer","untrusted_description":"\n\nKey Responsibilities\n-\nData & 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.\n\n-\nAnalytics Engineering: Standardize data modeling practices and build robust, reliable, scalable semantic layers that feed clean, trustworthy data to downstream analytics and reporting.\n\n-\nBusiness & 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.\n\n-\nBusiness Logic Translation: Reconciling data definitions across departments, a critical human-led task that AI cannot fully automate\n\n-\nData Quality & Governance: Establish automated testing, monitoring, and data quality frameworks to guarantee financial-grade reliability.\n\n-\nPerformance Optimization: Optimize query performance, warehouse costs, and pipeline orchestration.\n\nRequirements & Qualifications\n• Experience: 5+ years of experience in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment.\n\n• Technical Mastery:\n\nAdvanced SQL capabilities.\n\nHands-on expertise with modern data transformation and orchestration frameworks (dbt, Airflow, Prefect, Dagster).\n\nDeep experience with cloud data warehouses (Snowflake, Databricks, BigQuery).\n\n• AI Technical Stack:\n\nUnderstanding of feature engineering, AI agentic workflow lifecycles, and evaluation metrics.\n\nExperience with deploying AI agentic workflows in production environments.\n\n• Business Analysis Skills: Demonstrated ability to occasionally gather business requirements, define key metrics, and translate stakeholder needs into structured data assets.\n\n• Software Engineering Principles: Strong experience with version control (Git), CI/CD practices for data pipelines, and production code testing.","url":"https://jobs.lever.co/crypto/6c79c1c5-72d3-4bf1-9f9a-cfe4741d2c9c"}