Time since we first saw it
Day 12
Similar jobs usually come down between day 6 and day 47.
At Crypto, jobs usually come down by about day 26.
From 1,417 similar postings that came down, and 9 of Crypto’s. Coming down is not always a hire.
Senior AI Analytics Engineer
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingAs a Senior AI Analytics Engineer in our Data Team, you will bridge the gap between Data Engineering, Tech, and Product.
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.
Posting record
Report an errorActively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 23, 202612 days ago
- Recorded sightings
- 15
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Crypto is hiring
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down quickly
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- BigQuery
- Databricks
- dbt
- Prefect
- Snowflake
- Airflow
- Dagster
Where the posting names them
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.
• Technical Mastery: Advanced SQL capabilities. Hands-on expertise with modern data transformation and orchestration frameworks (dbt, Airflow, Prefect, Dagster).
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:
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).
Your next step
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Applying through Lever
- No account needed: one form, and Lever reads your resume into the profile recruiters search.
Already applied? Track this application
About the role
We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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.
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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.
Employment type
Full-time
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Hong Kong
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.