A DAG that fails silently at 3am and corrupts a week's worth of data is not an orchestration solution — it is a liability. Airflow is the standard for scheduling and orchestrating complex data and ML pipelines, but writing DAGs that are reliable, maintainable, and observable requires more than Python knowledge. Post your Airflow job on RemoteAI and hire an engineer who designs workflows that fail loudly, alert correctly, and recover gracefully.
Talent pool
These are the types of Airflow engineers available on RemoteAI. New profiles are reviewed by our team.
What pipelines need scheduling (data ingestion, dbt transformations, ML training, model deployment), the current Airflow setup or deployment target, and the main pain point — DAG failures, performance, scaling, or monitoring.
Not just engineers who have written basic DAGs but practitioners who have deployed Airflow on Kubernetes, designed fault-tolerant retry logic, and maintained DAGs in a team environment.
Look for readable DAGs with sensible task groupings, proper error handling, and observability built in. A DAG is code that runs forever — maintainability matters.
RemoteAI charges nothing on contracts.
Hiring a airflow engineer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
Airflow engineers on RemoteAI design and operate workflow orchestration systems for data and ML pipelines: writing well-structured DAGs using Airflow's operator library (PythonOperator, KubernetesPodOperator, DockerOperator), designing task dependency graphs and retry logic for fault tolerance, configuring XCom and Variable handling for inter-task data passing, deploying Airflow on Kubernetes (KEDA autoscaling, KubernetesExecutor) or cloud-managed Airflow (MWAA, Cloud Composer, Astronomer), and integrating with dbt, Spark, and ML training infrastructure. Strong Airflow engineers avoid common pitfalls like heavy imports at parse time and large XCom payloads.
A well-structured job post attracts the right airflow engineers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
What pipelines need scheduling (data ingestion, dbt transformations, ML training, model deployment), the current Airflow setup or deployment target, and the main pain point — DAG failures, performance, scaling, or monitoring.
Not just engineers who have written basic DAGs but practitioners who have deployed Airflow on Kubernetes, designed fault-tolerant retry logic, and maintained DAGs in a team environment.
Look for readable DAGs with sensible task groupings, proper error handling, and observability built in. A DAG is code that runs forever — maintainability matters.
RemoteAI charges nothing on contracts.
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Deployment environment (self-managed on Kubernetes, MWAA, Cloud Composer, Astronomer), the pipelines to be orchestrated (data, dbt, Spark, ML training), the scale (number of DAGs, task frequency), and whether this involves migrating from another orchestration tool.
Airflow has the widest adoption and richest operator ecosystem but requires more operational investment. Prefect and Dagster have better developer experience, type safety, and are easier to test locally. For teams with existing Airflow investments or that need specific operators like KubernetesPodOperator or MWAA, Airflow is the right choice. For greenfield projects prioritizing developer experience, Prefect or Dagster are worth evaluating.
Free for one job a month (open 14 days; jobs from companies without a verified website are reviewed first, paid plans for more). No commission on contracts.
KubernetesExecutor and ML pipeline orchestration specialists usually charge more. 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.
Sign up free and describe the pipelines, deployment environment, and main orchestration challenge. RemoteAI matches your post to Airflow engineers with production DAG design and deployment experience.