Hire Data Engineering developers
Hire data engineers who build reliable pipelines and warehouses with Airflow, dbt, Spark, BigQuery and Snowflake.
- Apache Airflow versionAirflow 3.3.2released 17 September 2026
- dbt Core versiondbt Core 1.12.5released 15 September 2026; dbt 2.0 in release-candidate stage
- Apache Spark versionSpark 4.2.0released July 2026
Checked September 2026 · PyPI (apache-airflow, dbt-core), Apache Spark news
Data engineering is the work of moving data from your apps, databases and third-party tools into one place where it can be trusted for reports, dashboards and AI. Developers build ETL and ELT pipelines with Apache Airflow for scheduling, dbt for SQL transformations and Apache Spark for large volumes, loading into warehouses such as BigQuery or Snowflake.
A good data engineer cares as much about tests, documentation and cost control as about getting data to move. You can hire a data engineer per project, by the hour from $10/hr, or monthly to run and extend your pipelines.
What a Data Engineering developer does
- Build and schedule ETL/ELT pipelines with Apache Airflow, including retries, alerts and backfills
- Model and transform warehouse data with dbt, with tests and documentation for each table
- Process large datasets with Apache Spark for batch jobs and joins that outgrow a single database
- Design warehouse schemas in BigQuery, Snowflake or PostgreSQL for reporting and analytics
- Connect sources such as MySQL, MongoDB, CRMs, ad platforms and payment gateways via APIs and connectors
- Monitor data quality and warehouse spend, and set access controls for sensitive fields
What businesses build with it
- A single sales and marketing dashboard combining CRM, ads and website data
- Daily finance and inventory reporting from ERP and e-commerce systems
- Clean, labelled datasets for machine learning and AI projects
- Migrating legacy reporting from spreadsheets or on-premise databases to a cloud warehouse
- Customer 360 tables that join orders, support tickets and app usage
Why teams choose it
- Gives everyone the same figures from one tested source instead of conflicting spreadsheets
- Open-source core tools (Airflow, dbt Core, Spark) avoid lock-in to a single vendor
- Pipelines as code are version-controlled, reviewable and repeatable
- Trade-off: pipelines need ongoing maintenance when source systems change, and cloud warehouse bills grow with poorly written queries, so budget for monitoring and upkeep, not just the first build
Roles people hire for
- Data Engineer
- Analytics Engineer (dbt)
- ETL Developer
- Big Data / Spark Developer
Common questions
Do we need a data warehouse, or is our database enough?
If you only report from one application, the existing database or a read replica may be enough. A warehouse helps once you combine several sources, need history or find reports slowing down your live system.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse, while ELT loads raw data first and transforms it inside the warehouse, often with dbt. ELT is common with BigQuery and Snowflake because they handle large transformations well.
BigQuery or Snowflake?
Both work well. BigQuery fits naturally if you already use Google Cloud, while Snowflake runs on several clouds; a developer should compare them on your data volume, query patterns and existing contracts.
Do we need Spark?
Not always. Many small and mid-sized businesses manage with SQL in the warehouse plus dbt; Spark becomes worthwhile when data volumes or processing jobs are too large for that approach.
Often used with
Hire Data Engineering developers
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