Senior Engineer - Data Engineering
Tasks
- Apply data governance for trusted outputs
- Build serverless data processing and orchestration workflows
- Create and optimize dashboards in Power BI
- Create feature stores semantic layers vector ready datasets
- Design build maintain ETL/ELT pipelines
- Develop optimize manage data models in cloud warehouses
- Document pipelines models and architectures
- Implement data quality checks monitoring alerting
- Ingest transform load cyber security data
- Integrate AI forecasting and anomaly indicators
- Tune pipeline queries and warehouse performance
Perks/Benefits
Skills/Tech-stack
AI Services | AWS ECS | AWS Fargate | AWS Lambda | AWS S3 | AWS SageMaker | AWS Step Functions | Amazon Redshift | Azure AI | Azure AI Services | Azure Blob | Azure Blob Storage | Azure Container | Azure Container Apps | Azure Data | Azure Data Factory | Azure Databricks | Azure Functions | Azure Synapse | Blob Storage | CI/CD | Container Apps | Data Factory | Data Governance | Data Quality | Data quality monitoring | Databricks | DevSecOps | Dimensional Modeling | ELT | EMR | ETL | Event Hubs | GitHub Actions | Google BigQuery | Infrastructure as Code | Kinesis | Looker | MSK | Power BI | Python | Quality monitoring | RAG | Serverless | Snowflake | Step Functions | Terraform | Vector Databases | Vertex AI | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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