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Mid-level, remote, generalist data-engineer/analyst role with broad skills attracts many qualified applicants.
E-commerce/DTC tooling increases domain specificity, though BigQuery/dbt skills remain transferable across industries.
Explicit 4–6 years, mandatory BigQuery/dbt/ETL skills and strict response SLAs.
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Own and maintain end-to-end data pipelines and infrastructure ensuring data accuracy, timeliness, and cost efficiency across multiple source systems.
Build and maintain automated data quality checks, validation processes, and trustworthy reporting dashboards for cross-functional teams including Growth, Marketing, Product, and Finance.
Analyze marketing and product performance data to provide actionable insights supporting business growth, promotional effectiveness, and leadership decision-making.
4–6 years of combined data engineering and analytics experience with proven production data pipeline maintenance and troubleshooting skills.
Strong expertise in Google BigQuery, advanced SQL (window functions, CTEs, complex joins), and experience with ETL tools (like Daton or Fivetran) and data transformation frameworks such as dbt.
Proficiency in at least one BI tool (e.g., Looker, Tableau, Power BI) and experience with e-commerce data sources such as Shopify, GA4, and ad platforms.
Response availability for critical issues: P0 within 2 hours, P1 within 4 hours, and P2 within 24 hours during Singapore business days.
Experienced hybrid data professional skilled in both data engineering and analytics with a focus on scalable pipeline reliability and actionable insights generation.
Comfortable working independently in a remote setting with strong ownership and clear communication across technical and non-technical stakeholders.
Demonstrated ability to manage complex e-commerce, DTC, or subscription data environments with focus on marketing KPIs, A/B testing support, and automation using AI or scripting tools.