Remote Data Engineer jobs

About the Data Engineer role

What the work looks like

• Writes batch and streaming pipelines in Python, SQL, or Spark and schedules them with an orchestrator such as Airflow.
• Models warehouse tables in dbt so that analysts get consistent definitions of customers, orders, and events.
• Adds tests and freshness alerts, then investigates when a job fails or a source schema changes.
• Tunes queries, partitioning, and cluster sizing to keep warehouse spend and run times under control.
• Agrees on data contracts with backend teams whose services produce the events and tables downstream users depend on.

What employers ask for

• SQL and Python at production quality
• Spark or another distributed processing engine
• Airflow, Dagster, or similar orchestration
• Snowflake, BigQuery, Redshift, or Databricks
• dbt and dimensional data modeling
• Kafka or other streaming platforms

Doing this job remotely

Pipelines run in the cloud and the work is reviewed through pull requests, so location rarely matters. Many teams share an on-call rotation for failed overnight jobs, which can mean early-morning pages depending on your time zone. Handling regulated data, such as health or financial records, can add device and location restrictions.

Where it comes from and where it leads

Common starting points are backend software engineering, database administration, and analytics roles where someone began automating their own SQL. Senior engineers move into data platform or architecture roles, analytics engineering leadership, or machine learning infrastructure.