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Online Business Intelligence Analyst Jobs (Work From Home)

Verified business intelligence analyst jobs with international employers. Remote, paid in USD, from $9/hr. Apply direct on FindTalent, free for talent.

0 open jobs · updated 27 Jul 2026$9–34/hr typical payFull-time · Part-time · GigEvery employer verified
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›Data & Analytics›Business Intelligence Analyst

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How much do online business intelligence analyst jobs pay?

Experience levelHourly (USD)Monthly (PHP, full-time)Typical setup
Entry level (0–1 yr)$9–14/hr₱86,000–133,000
Experienced (1–3 yrs)$14–22/hr₱133,000–209,000
Specialist (3+ yrs / niche)$22–34/hr₱209,000–323,000

Typical ranges for remote Filipino specialists, reviewed July 2026. Paid in USD, direct to you.

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Skills employers look for in business intelligence analysts

  • Advanced SQL and data warehouse proficiency: Window functions, CTEs, query optimization, and data warehouse SQL (BigQuery, Redshift, or Snowflake dialect) — the foundation of all BI work; verify with a complex query exercise in your specific warehouse environment.
  • BI tool mastery (Tableau, Power BI, or Looker): Not just dashboard building, but data source connection, calculated field design, and performance optimization for large datasets — review published dashboards and evaluate for design quality and usability.
  • Data modeling and dimensional design: Star schema, snowflake schema, fact and dimension table design — ask how they would model a simple e-commerce data warehouse with orders, customers, and products to support standard sales reporting.
  • ETL pipeline design and maintenance: dbt (data build tool), Fivetran, or custom Python ETL scripts — ask which ETL tooling they have used and how they handle a source system API change that breaks an existing pipeline.
  • Metric definition and documentation: Establishing a single source of truth for key business metrics — ask how they resolve a conflict when two teams are calculating the same metric differently and getting different answers.
  • Data quality monitoring: Automated data quality checks, freshness monitoring, and anomaly detection — ask how they would alert a stakeholder team if a daily data pipeline failed to load overnight.

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