Full-Stack Data Engineer
Tasks
- Adopt new data engineering technologies
- Apply software engineering best practices
- Build data products for business analytics
- Collaborate with architects and analysts
- Create automated retries and error handling
- Design scalable data pipelines
- Develop Python data processing services
- Develop SQL query optimization techniques
- Implement observability and incident response
- Implement orchestration and automation
- Improve engineering standards and reusable patterns
- Maintain service reliability and SLAs
- Manage environment promotion and release automation
- Monitor systems and conduct root cause analysis
- Perform data validation and performance tuning
- Run unit integration testing
- Set up CI/CD pipelines
- Translate business requirements into technical solutions
Perks/Benefits
- N/A
Skills/Tech-stack
API | AWS | AWS Glue | AWS Lambda | Apache Airflow | Automation Scripting | CDC | CI/CD | Collibra | DBT | Data Catalog | Data Modeling | Data Validation | DataOps | DevOps | Dimensional Modeling | Error Handling | Fivetran | Git | GitHub Actions | Incident Response | Incremental loads | Infrastructure as Code | Integration Testing | Lineage | Metadata | Monitoring | Observability | PySpark | Python | Query Optimization | Redshift | Release automation | Retry Mechanisms | SQL | Schema Design | Serverless computing | Snowflake | Starburst Trino | Terraform | Unit Testing | “as-code”
Education
N/A
Roles
Data Engineer | Engineer | Full Stack | Full Stack Data Engineer
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