Analytics Engineer
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Summary
Analytics Engineer who builds and owns the company-wide data infrastructure at Momcozy: designing a four-layer warehouse (ODS/DWD/DWS/ADS) on StarRocks and Hive, defining canonical business metrics like DAU/retention/GMV, and maintaining the semantic layer and data governance for de-identified app data serving non-China markets.
Location: Singapore
Employment Type: Full-time
Department: App Ecosystem Center — Platform Product Group, Data Product Team
Role Overview We are looking for an Analytics Engineer to build and own the data infrastructure that powers company-wide decision making. This role works with de-identified app data and serves markets outside of China, ensuring every number our teams see is consistent, trustworthy, and well-explained. You will design our warehouse architecture, define the metrics that the entire business runs on, and establish a single source of truth that ends cross-team disagreements over 'whose number is right.'
Who You'll Work With • Reports to: Data Product Lead (China HQ) or Regional Tech Lead • Collaborates with: Product managers, operations teams, customer support, data engineering team (HQ), and regional engineering teams • Team context: Bridges business stakeholders and engineering; works across regions with China HQ data platform team
Responsibilities • 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, enabling layered reuse of detail-level data, aggregated data, and application metrics • Map core business scenarios and build a company-wide standardized metrics framework; 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 • Build and maintain the semantic layer to unify metric definitions, dimension semantics, and business logic, shielding business users from underlying warehouse complexity • Establish end-to-end 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
Requirements • Bachelor's degree or above in Statistics, Data Science, Computer Science, Mathematics, or a related field, or equivalent industry experience • 3+ years of experience in data platform, analytics engineering, or data science at internet companies • 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 • Legally authorized to work in Singapore; this role does not provide visa sponsorship
Role Overview We are looking for an Analytics Engineer to build and own the data infrastructure that powers company-wide decision making. This role works with de-identified app data and serves markets outside of China, ensuring every number our teams see is consistent, trustworthy, and well-explained. You will design our warehouse architecture, define the metrics that the entire business runs on, and establish a single source of truth that ends cross-team disagreements over 'whose number is right.'
Who You'll Work With • Reports to: Data Product Lead (China HQ) or Regional Tech Lead • Collaborates with: Product managers, operations teams, customer support, data engineering team (HQ), and regional engineering teams • Team context: Bridges business stakeholders and engineering; works across regions with China HQ data platform team
Responsibilities • 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, enabling layered reuse of detail-level data, aggregated data, and application metrics • Map core business scenarios and build a company-wide standardized metrics framework; 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 • Build and maintain the semantic layer to unify metric definitions, dimension semantics, and business logic, shielding business users from underlying warehouse complexity • Establish end-to-end 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
Requirements • Bachelor's degree or above in Statistics, Data Science, Computer Science, Mathematics, or a related field, or equivalent industry experience • 3+ years of experience in data platform, analytics engineering, or data science at internet companies • 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 • Legally authorized to work in Singapore; this role does not provide visa sponsorship