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Browse remote ETL positions — data pipeline engineering, dbt transformation layer development, and ELT workflow automation at AI-first companies.
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Posted 6 months ago
Applications closedPolarML
Remote / Berlin
Berlin, Germany (UTC+1)
Our dbt project is a mess. Forty models, no tests, documentation nobody has touched in six months, and a naming convention that only one person unders...
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Posted 6 months ago
Applications closedSignalCove Data
Remote (EU preferred)
We collect sensor data from production lines, but our quality reporting is still built from manual CSV exports. It is slow, inconsistent, and nobody i...
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Posted 6 months ago
Applications closedNordic NLP
Remote
We run a large Hadoop cluster that's expensive, slow to maintain, and blocking our data team. We want to migrate our batch processing workloads to Dat...
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Posted 6 months ago
Applications closedStrata AI
Remote
Berlin, Germany (UTC+1)
Bad data costs us decisions. We need a data quality engineer to build the checks, alerts, and processes that keep our data warehouse reliable. You wil...
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Posted 6 months ago
Applications closedQuantumEdge
Remote
We're migrating from a legacy on-premise data warehouse (PostgreSQL + custom ETL scripts) to Snowflake with dbt for transformations and Airflow for or...
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Three data engineers, five ETL tools, zero documentation. Every time someone asks "where does this metric come from" we get three different answers. W...
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Role guide
ETL engineers build the data pipelines that make AI possible. Remote ETL jobs at AI companies involve designing ELT workflows with dbt, integrating data sources with Fivetran or Airbyte, and orchestrating pipelines with Airflow. Clean, reliable data is the most critical input for any ML system — and ETL engineers are the people who deliver it.
What remote ETL jobs are available at AI companies?
Data engineering and analytics engineering roles focused on pipeline development. Most require SQL and dbt alongside Python and a cloud data warehouse.
Is ELT replacing ETL in remote job listings?
In modern cloud-native stacks, yes. Most AI companies use ELT — load raw data first, transform in the warehouse with dbt. Traditional ETL (transform before load) is common in legacy enterprise environments.
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