About the Role

We are looking for an Analytics Engineer to build and own the data infrastructure that powers company-wide decision making. You will design warehouse architecture, define the metrics that the entire business runs on, and ensure every number our teams see is consistent, trustworthy, and well-explained.

What You'll Do

1. Data Warehouse Modeling & Layered Architecture

- Design and build a standardized four-layer warehouse architecture (ODS / DWD / DWS / ADS) on StarRocks, Hive, and related big-data components

- Model core business dimensions including user, device, campaign, app version, region, and acquisition channel

- Enable layered reuse of detail-level data, aggregated data, and application metrics to support analytics across all business lines

2. Unified Metrics Framework & Semantic Layer

- Map core business scenarios and build a company-wide standardized metrics framework

- Build and maintain the semantic layer to unify metric definitions, dimension semantics, and business logic

- Own canonical definitions for key metrics — user activity (DAU/WAU/MAU), PV/UV, retention, funnel conversion, campaign GMV, device error rate — including calculation rules, dimensions, and documentation

- Shield business users from underlying warehouse complexity; establish a single source of truth that ends cross-team disagreements over whose number is right

3. End-to-End Data Quality Governance

- Establish data governance mechanisms covering: event-tracking review, data validation, dirty-data cleansing, definition iteration, anomaly monitoring, and alerting

- Continuously reduce the share of invalid and anomalous data; keep pipelines stable, trustworthy, and fully traceable

What We're Looking For:

- Bachelor's degree or above in Statistics, Data Science, Computer Science, Mathematics, or a related field

- 3+ years of experience in data platform / analytics engineering / data science at internet companies

Core Competencies:

- Proven ability to build a business data system from 0 to 1, covering the full lifecycle: tracking plan design → event dictionary → dimensional modeling → semantic layer → metrics framework → data governance

- Hands-on experience abstracting business semantics, unifying dimensions, and driving metric standardization to production

Technical Skills:

- Strong SQL proficiency; experienced with StarRocks, MySQL, Hive (or comparable engines such as Snowflake, BigQuery, Redshift)

- Practical experience with complex log parsing, user journey reconstruction, and high-cardinality UV deduplication at scale