We source, vet, and manage hiring so you can meet qualified candidates in days, not months. Strong English, U.S. time zone overlap, and compliant hiring built in.
.svg){kind=logo}
Apache Airflow is the industry standard for orchestrating data pipelines at scale. It uses Directed Acyclic Graphs (DAGs) written in Python to define, schedule, and monitor complex workflows spanning multiple systems. If you're running data pipelines and need visibility, reliability, and the ability to handle retries and failures gracefully, an Airflow engineer from Latin America can build you a production-grade orchestration layer that handles millions of task executions. Start your search at https://www.hireinsouth.com/start.
Apache Airflow is a workflow orchestration platform written in Python that lets you define data pipelines as code using DAGs (Directed Acyclic Graphs). A DAG is essentially a Python file where you define tasks (units of work like SQL queries, API calls, data transformations) and their dependencies. Airflow then handles scheduling, monitoring, retries, and failure handling. It's used by companies like Netflix, Uber, Spotify, and Stripe to orchestrate petabyte-scale data operations.
Airflow's core strength is its Python-based definition language (no YAML spaghetti), powerful UI for monitoring task execution, rich operator ecosystem (operators for Kubernetes, Spark, AWS, GCP, Snowflake, etc.), and production-grade reliability features (retries, deadletters, circuit breakers). Version 2.0+ dramatically improved the developer experience with better type hints, simpler syntax, and cleaner architecture.
Unlike visual DAG builders or cloud-native alternatives (AWS Glue, Dataflow), Airflow gives you code that you version control, test, and deploy like any software project. This is powerful for teams with strong engineering practices and makes debugging and extending workflows natural.
Hire Airflow if you're orchestrating complex data pipelines with multiple dependencies, external system integrations, and the need for visibility into what's running, what failed, and why. Common scenarios: ETL pipelines ingesting data from multiple sources, scheduled analytics refreshes, data warehouse maintenance, ML training pipelines, and real-time event processing at scale.
Airflow is overkill if you have simple, linear batch jobs (a single cron job might suffice), or if your workflow is built into a single system (like dbt Cloud). It's also not ideal if your team is unfamiliar with Python or software engineering practices. If you need true real-time streaming (not batch), Kafka or Spark Streaming might be better.
Team composition: An Airflow engineer pairs well with data engineers (Spark, dbt), database specialists (SQL tuning), backend engineers (for building operators), and DevOps engineers (for Kubernetes deployment). For larger teams, you can have Airflow specialists focused on platform (shared DAGs, custom operators) and data engineers writing domain-specific DAGs.
Look for strong Python fundamentals first: decorators, context managers, async patterns, and the ability to write testable, maintainable code. Airflow is written in Python and extensible through Python, so language depth matters.
Data engineering knowledge is critical. They should understand SQL, data warehousing concepts (staging, dimension tables, slowly changing dimensions), ETL patterns, and how to design idempotent workflows (critical for retries and replays). Experience with any orchestration tool (Luigi, Prefect, Dagster) signals the right thinking.
Production operations mindset is essential. They should think about observability (logging, alerting), failure modes (what if a dependency is down?), and operational safety (how to replay data without corruption). Red flags: engineers who treat Airflow as a scheduler and don't think about the operational layer, or who can't reason about task dependencies and idempotency.
Junior (1-2 years): Should know Python basics, understand DAG structure, be able to write simple operators and tasks, understand scheduling, and basic task retry logic. They can build straightforward linear pipelines.
Mid-level (3-5 years): Should have shipped multiple production Airflow deployments, understand complex dependencies and XCom patterns (inter-task communication), be able to design idempotent workflows, write custom operators, tune performance, and set up monitoring. They can architect multi-team Airflow instances.
Senior (5+ years): Should be able to design large-scale orchestration platforms, mentor teams on Airflow best practices, optimize for cost and reliability, design disaster recovery, set up multi-region deployments, and make strategic decisions about when Airflow is the right tool vs. alternatives. They should have shipped systems orchestrating terabyte-scale operations.
Tell me about a time a pipeline failed in production. How did you debug it, and what safeguards did you put in place to prevent similar failures? Strong answer describes specific failure, systematic debugging process, and preventive measures (idempotency, better monitoring, dependency management).
Describe a time you had to replay data in a production Airflow pipeline without causing corruption. What was your approach? Look for understanding of idempotent design, backfill logic, and data integrity. Senior devs should mention impacts on downstream systems.
How do you approach monitoring and alerting for data pipelines? What metrics matter? Strong candidates discuss task success rates, SLAs, data quality checks, and alerting strategies. They should have concrete examples of dashboards they've built.
Tell me about a time you had to optimize a slow Airflow DAG. What bottlenecks did you identify and how did you fix them? Look for understanding of parallelization, task pooling, XCom patterns, and database optimization.
Describe a time you built a custom Airflow operator. What was the use case and what challenges did you encounter? Look for deep Airflow knowledge and practical problem-solving ability.
In Airflow, what's the difference between a Task and a TaskGroup? When would you use each? Good answer: tasks are atomic units of work, task groups organize related tasks. TaskGroups improve readability and allow higher-level dependencies. Senior devs discuss dynamic task generation.
Explain idempotency in the context of data pipelines. Why is it critical for Airflow? Strong answer: idempotent tasks produce the same result regardless of how many times they run. Critical for retries and replays. Examples: upserts vs. inserts, partition overwrites.
How do you handle inter-task communication in Airflow? What are XCom patterns and when should you use them? Good answer covers XCom (cross-communication), pulling results between tasks, and performance considerations. Senior devs discuss alternatives like shared storage or databases.
What's the difference between a Sensor and an Operator? Give examples of when you'd use each. Strong answer: operators execute work, sensors wait for conditions. Examples: PythonOperator, BashOperator, S3KeySensor, ExternalTaskSensor.
How would you design an Airflow deployment for a multi-team organization where different teams own different DAGs? Good answer covers DAG isolation, shared operators library, monitoring strategy, and governance. Senior devs discuss cost allocation and performance tuning.
Design and implement a multi-stage ETL pipeline in Airflow: extract data from an API, transform it (data quality checks, aggregations), and load into a database. Include error handling, retry logic, and monitoring. Should be idempotent so it can be replayed. Evaluation: Can they define a DAG, use operators, handle XCom, implement retries, and think about idempotency? Senior devs should consider performance, cost, and observability.
Latin America Airflow engineer salaries (2026) by seniority:
US-based Airflow engineers command 70-100% higher salaries, with senior engineers in tech hubs earning $160,000 - $240,000+. LatAm engineers offer 40-50% cost savings while delivering production-grade work. Brazil (Sao Paulo) and Argentina (Buenos Aires) have the strongest data engineering ecosystems. Colombia has competitive talent with growing data infrastructure expertise.
Latin America has a rapidly growing data engineering community, particularly in Brazil where companies like Nubank and Farfetch run massive data operations. Many LatAm engineers have built production Airflow systems handling billions of records. Argentina has strong data science traditions from universities like UBA, driving Airflow adoption.
Time zone matters for data operations: most LatAm engineers are UTC-3 to UTC-5, giving 6-8 hours of overlap with US East Coast teams. This is valuable for real-time issue resolution when pipelines fail.
LatAm engineers bring strong SQL and database foundations (often from polyglot backgrounds in Python, Java, Scala), and pragmatic problem-solving mentality. Many have worked with distributed systems at scale and understand infrastructure concerns.
English proficiency is high in the data engineering community, where most resources (documentation, Stack Overflow, conferences) are in English.
Share your requirements: DAG complexity, data volumes, team size, and seniority. We ask about production incidents, performance tuning, and multi-team orchestration to identify the right fit.
South matches you from our pre-vetted network of Airflow engineers. Every candidate has passed technical vetting (live DAG design, code review, architecture discussion) and has references from previous clients.
You interview matched candidates (typically 2-3 options) in a 30-minute screening call. We provide GitHub repositories, past DAGs (with sensitive data redacted), and technical scores.
Once you hire, South handles compliance, payroll, and support in the engineer's home country. Our 30-day guarantee means if the fit isn't right, we'll find a replacement at no additional cost. Start your search today.
Airflow is used for orchestrating data pipelines: ETL workflows, batch processing, data warehouse maintenance, ML training pipelines, and any repeating process that involves multiple steps with dependencies.
Airflow is ideal for complex pipelines with multiple dependencies and the need for visibility and reliability. It's less suitable for simple one-off jobs or true real-time streaming. Contact South if unsure about fit.
Airflow is the industry standard with the largest ecosystem. Prefect emphasizes simplicity and developer experience. Dagster emphasizes type safety and asset-centric thinking. Choose Airflow for scale and maturity; choose Prefect for ease of use; choose Dagster for sophisticated data assets.
Mid-level Airflow engineers from LatAm cost $65,000 - $92,000/year, roughly 40-50% less than comparable US talent. Senior engineers cost $95,000 - $145,000/year.
From initial conversation to job offer is typically 5-10 business days. We prioritize speed without sacrificing fit.
Greenfield Airflow deployments need mid-level or senior engineers who can design scalable systems. Maintenance or feature work on existing DAGs can be done by juniors under supervision.
Yes. South supports part-time and contract arrangements. Short-term projects (3-6 months) like Airflow platform optimization can be structured as contracts.
Most are UTC-3 to UTC-5 (Argentina, Brazil, Colombia), giving 6-8 hours of real-time overlap with US East Coast and 3-5 hours with US West Coast.
Every candidate passes resume review, live technical interview (designing a DAG under time pressure), code review of production DAGs, and reference calls with past clients. We vet 10-15 candidates to find 1 we recommend.
South offers a 30-day replacement guarantee. If the engineer isn't right for any reason, we'll vet and find a replacement at no additional cost.
Yes. South manages all payroll, tax compliance, benefits, and equipment in the engineer's home country. You pay one invoice monthly.
Absolutely. South frequently staffs entire data teams: Airflow orchestration specialists, dbt modelers, data analysts, and infrastructure engineers.
