Sr. Business Partner, Field Alliances Operations (Remote)
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Sr. Business Partner, Field Alliances Operations (Remote)
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Data engineers, analysts, and scientists build pipelines for global commerce and fintech, remotely. SQL fluency table-stakes; Python preferred. Median mid-level offer: $5,200/mo.
USD-paying remote data & analytics roles for Indonesian professionals. Currently 251 active positions across data engineer, analytics engineer, data analyst, BI engineer, data scientist, and analytics lead. Sources: eight global remote platforms. Every listing is pre-filtered — no US/EU work-permit requirement, fully remote, and Indonesia-based candidates are accepted. Dominant stack: advanced SQL, Python/pandas, dbt, Snowflake/BigQuery/Redshift, Airflow/Dagster/Prefect, Looker/Metabase/Mode, and one tier-1 BI tool. Dimensional modeling skill + business framing drive comp tier. Salary bands: $2,500–$5,000/month for junior data analyst, $5,000–$10,000 for mid/senior analytics & data engineer, $10,000–$18,000 for staff/lead, and $18,000–$25,000+ for principal data engineer or head of data. Apply directly to the employer via the link on each listing.
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Data analyst: query + dashboard + insight communication. Analytics engineer: model data with dbt and maintain a semantic layer for self-serve. Data engineer: build ingestion, transformation, and infra (Airflow, Snowflake). DE > AE > DA in pay typically.
Junior: $3,000–$5,000/month. Mid: $5,500–$9,000/month. Senior: $9,000–$15,000/month. Staff/principal: $15,000–$25,000+/month. Higher at fintech/AI startups due to data volume and complexity.
Advanced SQL plus one BI tool (Looker/Metabase/Mode) is enough for junior. Mid-senior also needs Python (pandas), dbt exposure, and stakeholder communication. Bonus: event-tracking implementation experience.
Snowflake or BigQuery (warehouse), dbt (transformation), Airflow or Dagster (orchestration), Fivetran/Airbyte (ingestion), Looker or Metabase (BI), and PostHog/Amplitude (product analytics). SQL + Python required.
Yes, if the product runs at scale (>5TB warehouse or >100 daily active dashboard users). Common gap: dbt and analytics-engineering practice. Bridge with a dbt side project on a public dataset.