Resolve 3 Dysfunctional Airflow DAGs (Databricks)

Title: Urgent: Debug & Fix 3 Failing Airflow DAGs (Databricks Notebook Orchestration) Description: I have 3 Airflow DAGs already deployed and visible in our Airflow instance, but they are currently failing on execution. I need a freelancer to diagnose the root cause of each failure and provide fixes so all three run successfully. The three DAGs: [Profile Load DAG] — [brief description if you have one] [Load DAG] — [brief description if you have one] [PAU DAG] — [brief description if you have one] Each DAG orchestrates a sequence of Databricks notebook tasks (batch acquire → config-driven processing steps → batch complete), reading from PostgreSQL-backed control/checkpoint tables. What I need: Review the failure logs/error traces for each of the 3 DAGs in Airflow Identify the root cause of each failure (could be config mismatches, permission issues, path/naming issues, parameter mismatches between tasks, or notebook-level bugs) Propose and implement fixes Confirm all 3 DAGs run end-to-end successfully after the fix Access provided: Airflow UI access, Databricks workspace access, relevant notebook and DAG code — shared privately with the selected freelancer. Ideal candidate: Experience with Apache Airflow (DAG debugging, task logs, Variable/connection troubleshooting), Databricks notebooks (PySpark/Python), and SQL-based control/checkpoint table patterns.