GenAI Data ETL Engineer
Tasks
- Build ETL ELT pipelines for LLM context
- Design semantic data models for RAG
- Ensure secure auditable and compliant access controls
- Implement PII redaction and normalization
- Implement data quality checks for GenAI retrieval
- Implement observability for data and retrieval
- Implement prompt response logging and retrieval traces
- Ingest structured and unstructured data sources
- Integrate SaaS and enterprise systems into data pipelines
- Maintain documentation lineage schemas and runbooks
- Optimize pipelines for reliability performance and cost
- Perform root cause analysis for pipeline failures
- Set up scheduling orchestration and workflow management
- Transform documents and records with chunking and enrichment
- Wire pipelines into LLM orchestration flows
Perks/Benefits
- Employee wellness programs
- Flexible work arrangements
- Health insurance
- Inclusive culture
- Learning and development opportunities
- Paid time off
- Retirement benefits
Skills/Tech-stack
AWS Glue | Airflow | Amazon Redshift | Azure Data | Azure Data Factory | Azure Synapse | BigQuery | CDC | CI/CD | CSV | Cloud Data | Cloud data platform | DBT | Dagster | Data Factory | Data Modeling | Data platform | ELT | ETL | Elasticsearch | Event Driven | Event-driven architecture | Git | JSON | Log Based Ingestion | Machine Learning | OpenSearch | PGVector | PII Redaction | Pinecone | Prefect | Python | RAG | REST API | SQL | Search index | Semantic Search | Snowflake | Vector Database | Weaviate | Webhooks
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science
Roles
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