Senior Data Management Professional - Data Engineering - Entities
Tasks
- Build feedback loops with domain experts to improve extraction accuracy workflow efficiency
- Collaborate with product and engineering to define platform architecture workflow requirements quality standards
- Design automated ingestion pipelines for entity data from documents
- Develop workflows for document parsing data extraction normalization validation enrichment publishing readiness
- Implement data lineage observability monitoring quality measurement frameworks
- Implement human in the loop review validation correction approval workflows
- Integrate AI LLM tools and rules based logic for responsible extraction automation
- Profile source documents and extracted datasets for quality gaps inconsistencies
Perks/Benefits
- 401k match
- Life insurance
- Long-term disability
- Medical/Dental/Vision insurance
- Paid Holidays
- Paid time off
- Short-term disability
- Wellness programs
Skills/Tech-stack
AWS | AWS EMR | AWS Glue | Amazon S3 | Apache Airflow | Apache Kafka | Apache Spark | Azure | Azure Data | Azure Data Factory | Azure Data Lake | Azure Data Lake Storage | CI/CD | Data Factory | Data Governance | Data Lake Storage | Data Lineage | Data Observability | Data Quality | Data Reconciliation | Data Validation | Data pipeline | Data profiling | Databricks | Document processing | Git | GitLab CI | Human-in-the-loop | Java | LLM | Language Processing | Metadata Management | Monte Carlo | NLP | Natural Language | Natural Language Processing | OCR | OpenLineage | Python | Rules based | Rules-based automation | SQL | Scala | The Loop
Education
Regions
Countries
States
Cities
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