Data Engineer Data Pipelines and ETL
Tasks
- Automate data validation anomaly detection and monitoring
- Build and maintain scalable batch and streaming data pipelines
- Build modular data components
- Design data models for analytics and ML
- Develop ETL and ELT frameworks
- Develop semantic layers for BI ML and AI
- Enforce governance privacy and security best practices
- Ensure compliance for AI data usage
- Establish data lineage and metadata standards
- Implement ingestion patterns CDC incremental loads event driven processing
- Implement query optimization indexing partitioning and performance tuning
- Optimize pipeline performance reliability scalability and cost
- Write production grade SQL for large scale analytics
Perks/Benefits
- 401k plan
- Disability benefits
- Life insurance
- Life insurance coverage
- Medical/Dental/Vision
- PTO
- Paid time off
- Tuition assistance
Skills/Tech-stack
Anomaly Detection | Apache Airflow | CDC | Cloud Composer | Data Governance | Data Lakehouse | Data Modeling | Data Observability | Data Quality | Data Warehousing | Databricks | Dimensional Modeling | Distributed Systems | ELT | ETL | Embeddings | Event Driven | Event-driven architecture | GCP | Google Cloud | Google Cloud Composer | Google Pub/Sub | Incremental loading | Kafka | ML pipelines | Pub/Sub | Python | SQL | Spark | Superset | Vector Databases | Vertex AI
Education
Roles
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