Data Engineer
Tasks
- Add metric descriptions context and query guidance
- Build automated AI readiness scoring pipeline
- Build data monitoring to detect issues
- Build semantic layer framework
- Collaborate with cross team partners to onboard data assets
- Collect data quality signals and publish readiness scores
- Document processes and contribute to shared knowledge base
- Enforce standards using automated validation
- Implement data contract and metadata standards
- Implement query accuracy checks and AI reliability guardrails
- Implement semantic modeling for consistent metrics
- Maintain tagging and lineage automation frameworks
- Monitor AI usage and define AI assistance avenues
- Provide attribution for AI answers
- Support sensitive data discovery and classification
- Translate privacy policies into data access guardrails
- Use AI tools to accelerate data engineering development
Perks/Benefits
Skills/Tech-stack
Agile | Configuration as Code | Data Catalog | Data Classification | Data Freshness Monitoring | Data Governance | Data Lineage | Data Observability | Data Privacy | Data Quality | Data freshness | Databricks | Freshness Monitoring | LLM | Language Processing | Medallion Architecture | Metadata Management | Natural Language | Natural Language Processing | Python | SQL | Schema Validation | Semantic Modeling | Semantic layer | Tagging | “as-code”
Roles
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