Data Engineering
Hire senior Apache Airflow engineers
Airflow runs the schedules the business depends on. Our seniors have operated large Airflow fleets: DAG design that survives failure, sensors that do not melt the metastore, and upgrades that do not break production on a Friday.
Shortlist of around 3 senior profiles in under 5 business days
- Seniority floor
- 7+ years owning production systems
- Time zone
- Full overlap with US Eastern to Pacific
- Shortlist
- Around 3 curated profiles in under 5 business days
- Engagement model
- Embedded in your team, no resellers or middlemen
- Pricing
- All-inclusive rate, around 35% below the US onshore equivalent
How we vet
What senior Apache Airflow actually looks like
DAG design: idempotent tasks, backfills, retries, SLAs, and alerting that pages the right person
Scaling: executors, pools, concurrency tuning, and metadata database hygiene
Dynamic pipelines, custom operators, and testing DAGs before they hit production
Managed flavors: MWAA, Cloud Composer, and Astronomer trade-offs
The roles that master Apache Airflow
Apache Airflow questions, answered
Is Airflow still the right choice in 2026?
For most production data platforms, yes. It has the deepest ecosystem. Seniors also know when Dagster or Prefect fit better, and will say so.
What breaks Airflow in production most often?
Non-idempotent tasks and metastore overload from bad sensor patterns. Both are things our vetting explicitly checks for.
Can they migrate us off a legacy scheduler to Airflow?
Yes. Migrating cron jobs, Control-M or Luigi setups into well-structured Airflow is a classic senior data engineering engagement.
Usually paired with Apache Airflow
Keep reading about hiring for Apache Airflow
Need senior Apache Airflow on your team?
Tell us the context. A curated shortlist of senior profiles in under five business days, working in your time zone.