Data & Knowledge Engineer

About the role

Provide trusted, contextual and well-governed enterprise data and knowledge services that ground agentic workflows and improve their reliability.

Key responsibilities

• Design and build ingestion, transformation and serving pipelines for structured and unstructured data.
• Create retrieval indexes, metadata models, semantic layers, knowledge graphs or data products as appropriate.
• Implement chunking, enrichment, lineage, quality and access-control patterns.
• Optimize retrieval quality, freshness, latency and cost with the AI engineering team.
• Integrate cloud and on‑premises data sources for hybrid solutions.
• Support evaluation datasets, monitoring data and traceability requirements.

Required profile

• 4+ years in data engineering, analytics engineering, information retrieval or knowledge platforms.
• Strong SQL and Python skills and experience with data pipelines, APIs and data modeling.
• Practical knowledge of vector search, embeddings, metadata, document processing and retrieval evaluation.
• Experience with enterprise security, data quality and hybrid data integration.

Required skills

• SQL
• Python
• Spark
• Microsoft Fabric
• Azure Data Factory
• Databricks
• Snowflake
• Azure AI Search
• PostgreSQL with pgvector
• Elasticsearch
• Pinecone
• Weaviate
• Milvus
• Neo4j
• RDF