Data Analyst

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.

Monday to Friday, 8:30 PM – 5:30 AM Manila Time

• 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

• 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

• 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

Originally posted on Himalayas