Data Engineer — Early Career
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Summary
An early-career data engineer builds, troubleshoots, and documents data pipelines and cloud-based data infrastructure, diagnosing failed pipelines and improving performance. Work is remote, project-based (about 2–3 weeks, 10–20 hrs/week), using tools like dbt, Kafka, Spark, Snowflake, or Redshift, paid per project in USD.
Build, troubleshoot and document data infrastructure and integration work.
What you will do
- Document pipeline architecture, transformations and data movement.
- Build and deploy cloud-based infrastructure and modern data tooling.
- Contribute asynchronously to integration and warehousing assignments.
- Diagnose failed pipelines and improve performance.
Required background
- 1–3 years in data engineering, ETL or a related professional role.
- Strong skills in at least one relevant technology, such as dbt, Kafka, Spark, Snowflake or Redshift.
- A bachelor’s degree in computer science, engineering, information systems or a related technical subject.
- Independent technical problem-solving.
Location and eligibility
- Remote work; country restrictions are not stated.
Availability
- Project-based asynchronous work over roughly 2–3 weeks, with 10–20 hours per week for accepted assignments.
Pay and engagement
Per project; fee agreed before work
Payment is for the agreed project. The fee and acceptance requirements are confirmed before you begin.
Amounts are in USD. The applicable rate or fee and work requirements are agreed before you begin.