Data Analyst

Position Summary  The Data Analyst will transform raw marketplace data into accurate, meaningful insights that support business decisions across commercial, supplier, and operations teams. Reporting to the Senior Data Analyst, the role will be responsible for maintaining recurring reports, conducting ad hoc analysis, developing dashboards, and creating visualizations that help leadership monitor platform performance and identify opportunities for improvement. This role is ideal for an early-career analyst with strong SQL skills, a curious and analytical mindset, and a desire to develop their data analytics skills in a fast-paced, data-driven marketplace environment.  Job Details  Work-from-home  Monday to Friday, 8:30 PM – 5:30 AM Manila Time  Will follow US holidays  Responsibilities  Write and maintain SQL queries in Snowflake to pull, join, and aggregate spend, catalog, supplier, and order data  Transform messy source data into clean, documented, reusable tables and views  Build and maintain dashboards in Sisense and Tableau that track GMV, supplier performance, take rate, and catalog coverage  Answer ad hoc questions from Supplier GTM, BD, Customer Relations, and Operations, turning vague asks into scoped analyses  Validate data quality, reconcile numbers across sources, and flag discrepancies before they reach stakeholders  Present findings in clear charts and short written summaries for non-technical audiences  Document query logic, table definitions, and metric calculations so work is repeatable  Support the Senior Data Analyst on larger analytics projects and gradually take ownership of workstreams  Qualifications  2 to 3 years of experience in a data analyst, BI, or similar role (strong internships count)  Strong SQL: joins, CTEs, window functions, aggregations, and debugging someone else's query  Hands-on experience with at least one BI tool (Tableau, Sisense, Power BI, Looker) and a good eye for chart choice and dashboard layout  Experience cleaning and transforming data, whether in SQL, Python (pandas), or Excel  Comfort with a cloud data warehouse such as Snowflake  Attention to detail: you check your numbers before you share them  Clear written and verbal communication, including explaining results to non-technical colleagues  Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering, math) or equivalent experience  Nice to have  Python for data wrangling or automation  Experience with dbt or another transformation framework  Exposure to marketplace, ecommerce, or procurement data (GMV, take rate, supplier and buyer metrics)  Familiarity with life sciences, lab supplies, or B2B purchasing  Experience with CRM data such as HubSpot or Salesforce  Comfort using AI tools to speed up querying and analysis