This is primarily a Data Platform Support Engineer role that combines:
Production support
Cloud platform operations
Data pipeline monitoring
Incident management
SQL and Python development
Data quality assurance
The role is focused on ensuring that enterprise data platforms used by retail clients run reliably and efficiently. Core Technical Skills Required 1.
Workflow
Orchestration
Airflow
You Should Be Comfortable With:
DAG creation and monitoring
Task dependencies
Scheduling and retries
Airflow Operators
Airflow logs troubleshooting
Failure handling
Control-M
Expected Knowledge
Job scheduling
Monitoring batch processes
s and notifications
Job dependency management
Restart and recovery procedures
Cloud
Technologies
AWS
Focus Areas
S3
Glacier
Storage lifecycle policies
Permissions and IAM
Data movement between buckets
Interview Questions
Difference between S3 and Glacier?
How do you recover archived data from Glacier?
What causes S3 access issues?
Azure
Key Services
Azure Databricks
Clusters
Jobs
Notebooks
Spark basics
Azure Data Factory
Pipelines
Triggers
Linked services
Monitoring
Azure Data Lake
Data storage architecture
Security concepts
Azure Synapse
Data warehousing
Query optimization
ETL integration
Interview Questions
What happens when an ADF pipeline fails?
How do you troubleshoot a Databricks job failure?
Difference between Data Lake and Synapse?
Interview Questions
How does BigQuery differ from traditional databases?
What are partitioned tables?
SQL Server & Data Modeling This is likely one of the most significant skills for the role.