{"id":3066160,"url":"https://alion.io/job/crypto-com-senior-ai-analytics-engineer","title":"Senior AI Analytics Engineer","company":{"id":2746,"name":"Crypto.com","domain":"crypto.com","url":"https://alion.io/company/crypto-com","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":86,"open_postings":14,"ghost_share":0,"stale_share":0.571,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hong Kong"],"countries":["HK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":121000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3160},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AI Agents","optional":true}],"status":"live","first_seen_at":"2026-10-22T07:45:11Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-25T12:48:35Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nAs 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.\nIn 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.\nKey Responsibilities\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\nAnalytics Engineering: Standardize data modeling practices and build robust, reliable, scalable semantic layers that feed clean, trustworthy data to downstream analytics and reporting.\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\nBusiness Logic Translation: Reconciling data definitions across departments, a critical human-led task that AI cannot fully automate\n\nData Quality & Governance: Establish automated testing, monitoring, and data quality frameworks to guarantee financial-grade reliability.\n\nPerformance Optimization: Optimize query performance, warehouse costs, and pipeline orchestration.\n\nRequirements & Qualifications\nExperience: 5+ years of experience in Data Engineering, Analytics Engineering, or Data Science, with at least 2 years in an AI environment.\nTechnical Mastery:\nAdvanced SQL capabilities.\nHands-on expertise with modern data transformation and orchestration frameworks (dbt, Airflow, Prefect, Dagster).\nDeep experience with cloud data warehouses (Snowflake, Databricks, BigQuery).\nAI Technical Stack:\nUnderstanding of feature engineering, AI agentic workflow lifecycles, and evaluation metrics.\nExperience with deploying AI agentic workflows in production environments.\nBusiness Analysis Skills: Demonstrated ability to occasionally gather business requirements, define key metrics, and translate stakeholder needs into structured data assets.\nSoftware Engineering Principles: Strong experience with version control (Git), CI/CD practices for data pipelines, and production code 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