Lead Data Engineer & Operations Support
RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO, Canada
CAD 90K-135K (estimate) Senior-level Full Time
Tasks
- Architect dashboards and executive reporting
- Build data transformation logic
- Define AI ML feature requirements
- Define data contracts
- Document troubleshooting procedures
- Drive production readiness reviews
- Enforce data quality standards
- Establish best practices for data quality
- Implement caching indexing and data partitioning
- Lead UAT and acceptance criteria
- Lead continuous improvement cycles
- Lead requirements definition
- Maintain data connectivity and integration
- Mentor analysts and engineers
- Monitor data pipelines
- Own source to target data lineage
- Perform post-implementation validation
- Translate business needs to technical specifications
- Troubleshoot database and API connections
- Troubleshoot incidents and data access issues
- Validate source-to-target mappings
Perks/Benefits
- Bonuses
- Casual dress environment
- Coaching
- Collaborative work environment
- Flexible benefits
- Networking opportunities
- Pension plan
- Professional development opportunities
Skills/Tech-stack
Acceptance criteria | Aggregation Pipeline | Amazon S3 | CI/CD | Dashboards | Data Contracts | Data Modeling | Data Pipeline Monitoring | Data Quality | Data Validation | Data Warehousing | Data pipeline | Debezium | Delta Lake | ELT | ETL | Elasticsearch | Embeddings | Feature Engineering | GitHub Actions | GraphQL | Iceberg | Kafka | Kibana | Kubernetes | Lakehouse | Logstash | Machine Learning | Machine Learning Pipelines | Model Training | Model training pipelines | MongoDB | OpenShift | Performance Tuning | Pipeline monitoring | Power BI | Query Optimization | RAG | Real Time | Real-time Inference | SQL | Schema Registry | Source to Target | Source-to-Target Mapping | Tableau | Target Mapping | Training pipelines | UAT | Window Functions
Education
N/A
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