DBT Cloud Data Engineer
Tasks
- Build scalable ELT architecture with layered data models
- Configure monitoring logging alerting and observability for pipelines
- Design data warehouse and data lake pipelines
- Develop dbt models and reusable macros
- Develop reusable ETL frameworks using Python
- Implement ETL ELT data pipelines in cloud
- Implement dbt production workflows with environment management
- Implement serverless orchestration for automation
- Integrate DBT with orchestration tools
Perks/Benefits
Skills/Tech-stack
API | AWS | AWS EMR | AWS Glue | AWS Lambda | AWS Step Functions | Alerting | Amazon Redshift | Amazon S3 | Apache Airflow | Authentication | Authorization | Azure | Azure Data | Azure Data Factory | Azure Data Lake | Azure Data Lake Storage | Azure Databricks | Azure Functions | Azure Synapse | Azure Synapse Analytics | BigQuery | CI/CD | Cloud Dataflow | Cloud Run | DBT | Data Factory | Data Lake | Data Lake Storage | Data Pipelines | Data Warehouse | ELT | ETL | GCP | IAM | Infrastructure as Code | Logging | Monitoring | Observability | PySpark | Pytest | Python | Snowflake | Step Functions | Synapse Analytics | “as-code”
Education
N/A
Roles
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