Analytics Engineer

remotepeople; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

remotepeople is seeking an Analytics Engineer to build and own the data infrastructure powering company-wide decision making. You will design warehouse architecture, define metrics, and ensure every number is trustworthy and well-explained.

You'll implement a four-layer warehouse (ODS/DWD/DWS/ADS) using StarRocks, Hive, and related tools; establish a unified metrics framework and semantic layer; and enforce data governance to keep pipelines stable and auditable across the business.

Qualifications

• 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.

Responsibilities

• Data Warehouse Modeling & Layered Architecture across ODS/DWD/DWS/ADS.
• Unified Metrics Framework & Semantic Layer development and maintenance.
• End-to-End Data Quality Governance including validation, anomaly monitoring, and alerting.

Skills

Data modeling

SQL proficiency

Data governance

Big data analytics

Education

Bachelor's degree in Statistics/DS/CS/Math

Tools

StarRocks

MySQL

Hive

Snowflake

BigQuery

Redshift

Job description

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. 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. 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. 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