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Data Engineer - Quality Assurance

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djinni.co
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Що робити

  • Automate data quality and reconciliation checks across varied storage layers, including Snowflake, SQL, and RDF/SPARQL databases
  • Test and verify data lineage, governance, and visualization components using Snowflake, data catalogs (ie. DataHub), Thoughtspot, and other visualization tools
  • Integrate test suites into the core infrastructure orchestrated by Apache Airflow and utilizing Iceberg table formats, while monitoring data pipeline health, alerting, and observability metrics using Prometheus and Grafana Cloud
  • Establish AI Evaluation Loops (Evals) and Guardrails: Build rigorous verification protocols— including structural tests, checks, and watchdog agents—to validate AI-generated artifacts, catch false positives, and ensure all automated outputs are secure, reliable, and free from hallucinations.
  • Integrate automated testing workflows into CI/CD pipelines using GitHub Actions, ensuring continuous stability and quality gates across all deployment environments

Що очікуємо

  • Data engineering & data testing: dbt, data lakehouse concepts, Medallion architecture
  • Databases & storage testing: SQL, Snowflake, AWS S3, Iceberg
  • Integrating quality check into data pipelines: Apache Airflow
  • API testing & automation: REST/OpenAPI, GraphQL
  • Integrating test automation into CI/CD: GitHub Actions (or similar like ArgoCD/GoCD)

Що пропонуємо

  • Graph databases: RDF / SPARQL
  • Data governance & analytics tools: DataHub, Thoughtspot
  • AI/ML testing & MLOps: AI evals, guardrails, RAG, vector databases, AI drift monitoring
  • Advanced / emerging data tech: StarRocks, DuckDB
  • Regulated environments: GxP, 21 CFR Part 11, HIPAA

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