Prinicipal, Data Engineer
Tasks
- Build and modernize end to end data pipelines
- Create reusable engineering standards and frameworks
- Define enterprise data engineering architecture
- Design and deliver scalable data platforms
- Drive automation using metadata management and reusable components
- Ensure security governance data quality and privacy compliance
- Establish production operations monitoring and incident response
- Implement batch streaming and event driven integration patterns
- Lead cross functional technical alignment and influence decisions
- Mentor engineers and raise data engineering maturity
- Translate business needs into scalable technical solutions
Perks/Benefits
- Accessibility
- Cafeteria
- Child care
- Coaching
- Domestic/international travel
- Employee discounts
- Employee events
- Flexible working hours
- Health benefits
- Onsite parking
Skills/Tech-stack
Access Controls | Apache Spark | Automated testing | Azure | Azure Databricks | CI/CD | Cloud Native | Cost Management | Data Governance | Data Lakehouse | Data Mart | Data Processing | Data Quality | Data Security | Data Warehouse | Data pipeline | DataOps | Delta Lake | DevOps | Distributed data | Distributed data processing | Docker | Event Driven | Event-driven architecture | Git | Infrastructure as Code | Kubernetes | Logging | Message Queues | Metadata Management | Monitoring | Observability | Performance optimization | Power BI | PySpark | Python | Qlik | Reliability Engineering | Retrieval technologies | Retrieval-Augmented Generation | SQL | Scala | Security Compliance | Semantic layer | Streaming | Tableau | Vector Databases | Workflow Orchestration | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Data Engineer | Engineer | Principal | Principal Data Engineer
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