Data and AI

Apache Airflow Orchestration

Schedule, sequence, and monitor the multi-step pipelines a real data platform depends on

16h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can design DAGs that correctly express task dependencies, choose appropriate operators for different task types, configure sensible retry and alerting behaviour, and monitor and troubleshoot real pipeline runs in Airflow.

Job titles this qualifies you for

Data EngineerAnalytics EngineerData Platform Engineer
Market intelligence
Demand
High
Remote
72% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$114,500–$137,500/year for the typical remote mid-level range (25th-75th percentile); average around $130,000-$148,000/year; senior remote data engineers average $191,822/year

Lessons

Stage 1 — Foundation
1
DAGs and Task Dependencies
40 min
2
Operators and Choosing the Right Task Type
35 min
Stage 2 — Applied
1
Scheduling, Retries, and Alerting
35 min
2
Monitoring and Troubleshooting Real Pipeline Runs
30 min
Tier 2 Proof Submission

Apache Airflow Orchestration Portfolio

Demonstrate genuine DAG design, operator selection, scheduling/retry/alerting configuration, and troubleshooting competence.

1.A DAG design with parallel and sequential task identification
2.An operator/sensor choice explanation for a file-dependent pipeline
3.A retry/alerting configuration for an API-dependent task
4.A troubleshooting walkthrough and a backfill-versus-forward-only explanation
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