Search & Pricing Pipeline
Orchestrates search ranking model training and pricing recommendations across 200+ countries. Coordinates data from listings, bookings, and market conditions.
Content Recommendation System
Processes billions of user engagement events daily across 10,000+ DAGs. Powers personalized content feeds and advertiser reporting.
Financial Data Processing
Orchestrates market data ingestion, portfolio calculations, and regulatory reporting. Time-critical pipelines with zero-tolerance for data errors.
Ride Analytics Platform
Coordinates ride data across multiple cities for surge pricing, driver earnings, and safety reporting. Multi-jurisdiction regulatory compliance.
What Airflow Engineers Actually Build
Before writing your job description, understand what an Airflow engineer does at scale. Here are real examples from companies that pioneered modern data infrastructure:
Financial Services & Fintech
Robinhood uses Airflow to orchestrate their market data pipelines—processing millions of stock trades and ensuring accurate portfolio calculations. Their Airflow engineers handle:
- Time-sensitive scheduling (markets open at specific times)
- Data quality validation between pipeline stages
- Recovery from upstream failures without data loss
Square runs Airflow for merchant analytics, coordinating data from payment terminals, invoices, and banking services. They manage:
- Cross-system data consistency checks
- SLA monitoring for business-critical reports
- Cost optimization across compute resources
Consumer Tech & Marketplaces
Airbnb (Airflow's birthplace) orchestrates data across 200+ countries:
- Search ranking model training pipelines
- Pricing recommendation updates
- Host payout calculations
- Regulatory compliance reporting
Pinterest runs 10,000+ DAGs processing billions of events:
- User engagement analytics
- Content recommendation systems
- Advertiser reporting pipelines
- ML feature engineering workflows
Logistics & Mobility
Lyft coordinates ride data across multiple cities:
- Real-time surge pricing inputs
- Driver earnings calculations
- Safety incident processing
- Regulatory reporting per jurisdiction
Understanding DAGs: The Core Concept
What Recruiters Need to KnowA DAG (Directed Acyclic Graph) is the fundamental unit of work in Airflow. Think of it as a flowchart that defines:
- What tasks need to run (extract data, transform it, load it somewhere)
- In what order (can't transform data before extracting it)
- When (daily at 6 AM, every hour, when upstream data arrives)
Why DAG Design Matters
Junior engineers create DAGs that "work." Senior engineers create DAGs that:
- Recover gracefully when one step fails
- Scale efficiently without overwhelming databases
- Are testable with clear inputs and outputs
- Are maintainable by other team members
A common interview signal: ask about idempotency. Strong candidates immediately understand why a pipeline should produce the same result whether it runs once or five times (retries after failures).
Airflow vs. Modern Alternatives
The Landscape
Airflow isn't the only option anymore. Understanding the alternatives helps you assess candidates and make better technical decisions:
| Tool | Best For | Trade-off |
|---|---|---|
| Airflow | Complex enterprise orchestration | Steeper learning curve, operational overhead |
| Dagster | Data asset-focused workflows | Newer, smaller talent pool |
| Prefect | Cloud-native simplicity | Less battle-tested at scale |
| dbt | SQL-only transformations | Not for general orchestration |
| Mage | Real-time + batch hybrid | Very new, limited enterprise adoption |
What This Means for Hiring
If candidates mention evaluating alternatives, that's a positive signal—it shows they think critically about tool selection. If they've only used Airflow and dismiss alternatives without understanding them, that's a potential red flag for adaptability.
The best Airflow engineers understand that orchestration principles transfer. Whether you use Airflow, Dagster, or Prefect, the concepts of task dependencies, retry logic, and data lineage remain consistent.
Recruiter's Cheat Sheet: Spotting Great Candidates
Conversation Starters That Reveal Skill Level
Instead of asking "Do you know Airflow?", try these:
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "How do you handle a DAG that fails at 3 AM?" | "I fix it and rerun" | "Idempotent tasks with automatic retries, alerts to on-call, backfill capability without duplicating data" |
| "Your pipeline takes 4 hours but needs to finish in 2. What do you do?" | "Run on a bigger machine" | "Profile bottlenecks, parallelize independent tasks, consider data partitioning, evaluate if batch is the right approach" |
| "How do you test DAGs before production?" | "Run them and see if they work" | "Unit tests for custom operators, integration tests with sample data, staging environment validation" |
Resume Signals That Matter
✅ Look for:
- Specific pipeline metrics ("orchestrated 500+ DAGs processing 10TB daily")
- Experience with managed services (MWAA, Astronomer, Cloud Composer)
- Mentions of data quality, monitoring, or SLA management
- Cross-functional experience (working with analysts, ML engineers)
🚫 Be skeptical of:
- "Airflow expert" without specific project details
- Only tutorial-level experience (simple ETL from CSV to database)
- No mention of failure handling or monitoring
- Listing every orchestration tool (Airflow AND Dagster AND Prefect AND Luigi)
GitHub and Portfolio Red Flags
- DAGs that run sequential tasks when they could be parallel
- No error handling or retry configuration
- Hardcoded credentials in code (security issue)
- No documentation or README explaining the pipeline
The Modern Airflow Stack (2024-2026)
Managed Services vs. Self-Hosted
Most companies now use managed Airflow services rather than running their own infrastructure:
Amazon MWAA (Managed Workflows for Apache Airflow)
- AWS-native integration
- Used by fintech companies for compliance
- Auto-scaling but can be expensive
Google Cloud Composer
- GCP-native, BigQuery integration
- Strong for ML/AI workflows
- Good for companies already on GCP
Astronomer
- Multi-cloud flexibility
- Enterprise features (RBAC, audit logs)
- Popular with companies needing SOC 2 compliance
Skills Hierarchy
- Table Stakes: Write DAGs, understand scheduling, use basic operators
- Mid-Level: Custom operators, sensor patterns, cross-DAG dependencies
- Senior: Performance optimization, platform administration, migration planning
- Staff/Principal: Architecture decisions, cost optimization, team enablement
Common Hiring Mistakes
1. Requiring Airflow Experience for Data Engineers
Airflow is a tool. Data engineering is a discipline. A data engineer who built pipelines with Luigi, Prefect, or even cron jobs can learn Airflow in 2-3 weeks. What takes years to develop: understanding data modeling, handling failures gracefully, designing for scale.
Pinterest's approach: They hire for data engineering fundamentals and train on Airflow.
2. Over-Testing Airflow Syntax
Don't ask "What's the difference between a Sensor and an Operator?" (that's documentation). Instead: "You notice a DAG that was completing in 30 minutes now takes 3 hours. Walk me through your debugging process."
3. Ignoring Operational Skills
Airflow at scale requires more than writing DAGs:
- Monitoring (Datadog, Grafana, built-in metrics)
- Debugging (log analysis, task isolation)
- Capacity planning (worker sizing, parallelism limits)
4. Not Considering Managed Services
If you're hiring someone to run self-hosted Airflow, you're also hiring for DevOps skills. If you're using MWAA or Cloud Composer, focus on pipeline development skills instead.
Assessing Real-World Competence
The Take-Home That Works
Instead of abstract coding challenges, give candidates a realistic scenario:
"Here's a simple DAG that extracts data from an API, transforms it, and loads it to a data warehouse. It currently works but has several issues:
- If the API fails, the whole DAG fails
- It processes all data every run (slow and expensive)
- There's no monitoring or alerting
- The transformation logic is hard to test
Improve this DAG and explain your changes."
This tests:
- Practical problem-solving
- Understanding of idempotency and incremental processing
- Production readiness mindset
- Communication skills (explaining trade-offs)
What to Listen For
Strong candidates will ask clarifying questions:
- "How often does this run? How much data?"
- "What's the acceptable latency for downstream consumers?"
- "Are there SLAs I should know about?"
- "What's the team's monitoring setup?"
These questions demonstrate that they understand pipelines exist in a broader system, not in isolation.