Senior AI Analytics Engineer
Summary
Senior Analytics Engineer on the data team of a crypto company: builds production data models, semantic layers, and data quality frameworks, uses LLMs to generate documentation and SQL/Python transformations, and occasionally acts as a technical business analyst. Core stack: SQL, dbt/Airflow-style orchestration, cloud warehouses (Snowflake/Databricks/BigQuery).
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
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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.
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Analytics Engineering: Standardize data modeling practices and build robust, reliable, scalable semantic layers that feed clean, trustworthy data to downstream analytics and reporting.
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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.
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Business Logic Translation: Reconciling data definitions across departments, a critical human-led task that AI cannot fully automate
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Data Quality & Governance: Establish automated testing, monitoring, and data quality frameworks to guarantee financial-grade reliability.
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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.
Skills
As published by lever · 8 questions
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, GitHub URL
Short answers (3)
- What is your Expected Annual Salary (HKD)? Please put numbers only.
- How long is your Notice Period?
- Where are you currently based? (City)
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- Do you require a visa sponsorship to work in Hong Kong?
- Have you ever been convicted of a crime or been subject to any sanctions by a regulatory body?
- Do you have any relatives currently employed by our company?
- How many years of relevant working experience do you have to the role?