ANALYTICS ENGINEER

GENIUS BUSINESS CONSULTANCY SERVICE PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

GENIUS BUSINESS CONSULTANCY SERVICE PTE. LTD. is seeking an Analytics Engineer to build and own the data infrastructure that powers decision making across the company.

You will design our warehouse architecture, define core metrics, and ensure every number is consistent, trustworthy, and well explained. You will model dimensions such as user, device, campaign, region, and acquisition channel, enable layered data reuse, and establish governance processes to monitor quality, validation, and

Qualifications

• Experience building data warehouses and analytics infrastructure.
• Ability to define and document metrics.
• Experience with data governance and quality controls.

Responsibilities

• Design and implement a four-layer warehouse architecture (ODS/DWD/DWS/ADS).
• Develop a company-wide metrics framework and semantic layer.
• Place data quality governance, validation, anomaly monitoring, and documentation at the center of pipelines.

Skills

Data modeling

SQL/ETL

Data governance

Tools

StarRocks

Hive

Job description

We are looking for an Analytics Engineer to build and own the data infrastructure that powers company-wide decision making. You will design our 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:

Data Warehouse Modeling & Layered Architecture

• Design and build a standardized four-layer warehousearchitecture (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

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"

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