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# Analytics Engineer

- **Company:** AI Powered LLC
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, BigQuery, Content Analysis, Information Engineering, Operational Data Store, Sql Optimization, Snowflake, Backend
- **Published:** September 23, 2026
- **Apply:** https://www.thejobnetwork.com/job/a32b5155-2b5a-4758-8496-795bf9200c4b/senior-analytics-engineer-mandarin-speaker

## About the Role

Requirements: 4 - 10 years of experience in analytics engineering or data engineering (Mandatory) Built data models from raw events at a VC-backed startup or reputable consumer company (e.g., TikTok, Instagram, Snap) (Mandatory) Experience at a consumer social, gaming, or creator platform (Nice-to-have) Built analytics engineering function at early-stage startup (Nice-to-have) Advanced SQL proficiency for designing reusable canonical data models (Mandatory) Experience with BigQuery, Snowflake, or similar modern warehouses (Mandatory)

## Description

We are looking for a Senior Analytics Engineer with 4+ years of experience to build the data modeling and analytics layer from the ground up at a fast-growing consumer social platform. This is a founding role - you'll own the transformation layer that turns raw product events, backend logs, and operational data into clean, reliable datasets powering product analytics, experimentation, growth insights, and company-wide metrics. You'll work closely with product, engineering, growth, and leadership to define how the company understands user behavior, content performance, and retention. This is ideal for someone who thrives in ambiguity, loves building practical v1s quickly, and wants outsized ownership at an early-stage company.

What you will be doing

 * Designing and maintaining core analytics data models that transform raw events and logs into clean, trusted, analysis-ready tables
 * Defining and operationalizing company-wide metrics - DAU, retention, creator supply, content engagement, funnel conversion, and growth efficiency
 * Building dashboards and self-serve data products that help teams track product performance and diagnose issues quickly
 * Partnering with product and engineering to improve event taxonomy, instrumentation quality, and data consistency across app, web, and backend
 * Establishing data quality standards, testing, documentation, and freshness checks so the team can trust the numbers


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