ETL and data pipeline engineers are the plumbers of your data infrastructure. They move, transform, and load data between systems reliably and at scale. RemoteAI connects you with ETL specialists who have shipped production data pipelines, not just configured a few Fivetran connectors.
Not ready to post a job?
Get up to 5 matched ETL Developers & Data Pipeline Engineers, free
Tell us what you need. We pick profiles we would contact ourselves and email them to you within 2 business days. No account needed.
Describe the role in a short form. One job a month is free with a company account: open 14 days, reviewed first if your website is not verified.
Open a profile and send a message, no job post needed. A new contact costs 10 credits, returned if there is no reply within 72 hours.
Chat on RemoteAI. With a job posted, invite the people you like to a video interview.
RemoteAI takes no commission on the contract you agree with the talent. Ready-made services show their service fee up front.
ETL and data pipeline engineers are the plumbers of your data infrastructure. They move, transform, and load data between systems reliably and at scale. RemoteAI connects you with ETL specialists who have shipped production data pipelines, not just configured a few Fivetran connectors.
ETL developers design, build, and maintain pipelines that extract data from source systems (databases, APIs, SaaS tools, event streams), transform it into clean, structured formats, and load it into destinations like Snowflake, BigQuery, Redshift, or data lakes. Modern ETL engineers often work with an ELT pattern, using tools like Fivetran, Airbyte, or Stitch for extraction, and dbt for SQL-based transformation. They also build custom Python pipelines with Airflow or Prefect for complex transformations, handle incremental loads, manage schema evolution, and implement data quality testing.
Finding the right ETL developer requires more than evaluating technical skills — it means identifying someone who ships production-ready work and understands your business goals. Here is exactly how to do it.
List every system that produces or consumes data: databases, SaaS tools, APIs, event streams, BI tools. This map is the foundation for scoping an ETL engagement.
For common SaaS sources (Salesforce, Shopify, HubSpot), a managed connector (Fivetran, Airbyte) plus dbt is faster and cheaper than custom code. For complex transformations, custom Python pipelines with Airflow give more control.
Ask how they handle schema changes, null values, and duplicate records in production pipelines. Engineers who have not dealt with real production data quality issues often underestimate this area.
Delivering clean, tested data from one key source system to your data warehouse is the right first milestone. It validates the architecture and integration approach before you scale to all sources.
ETL (Extract-Transform-Load) transforms data before loading it into the destination. ELT (Extract-Load-Transform) loads raw data first and transforms it inside the data warehouse using dbt. ELT is now dominant for cloud data warehouses because compute is cheap and raw data preservation is valuable.
Senior ETL consultants with Airflow, dbt and multi-source integration experience charge more than mid-level ETL developers; scoped projects are often priced as a fixed fee. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
Airflow is powerful but complex to operate. For simpler pipelines, Prefect or Dagster offer better developer experience. If you are on Databricks, Delta Live Tables handles orchestration natively. Choose based on pipeline complexity and your team's operational capacity.
ML models are only as good as the data they train on. ETL engineers build the pipelines that prepare training data, compute features, and keep model inputs fresh. An ETL engineer is typically needed before you can build a reliable production ML system.
Extraction: Fivetran, Airbyte, Stitch. Transformation: dbt (SQL), Python. Orchestration: Airflow, Prefect, Dagster, Databricks Jobs. Destinations: Snowflake, BigQuery, Redshift, Delta Lake. Quality: Great Expectations, dbt tests.