Data & Knowledge Engineer
# Data & Knowledge Engineer **PwC** · București · `On-site` 🕒 **Статус:** *Опубликовано: 13 дней назад* · *Источник: Indeed* --- ### About the Role Job Description & Summary The opportunity Provide trusted, contextual and well-governed enterprise data and knowledge services that ground agentic workflows and improve their reliability. What you will be doing - 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. What we need from you - 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. Relevant AI technologies and tooling - Strong SQL and Python capability with practical experience in Spark and data engineering platforms such as Microsoft Fabric, Azure Data Factory, Databricks, Snowflake or equivalent. - Hands-on experience processing structured and unstructured content, including parsing, OCR, chunking, enrichment, metadata extraction, lineage and incremental indexing. - Experience with vector and hybrid search technologies such as Azure AI Search, PostgreSQL with pgvector, Elasticsearch, Pinecone, Weaviate, Milvus or equivalent. - Understanding of embedding selection, semantic and lexical retrieval, metadata filtering, reranking, query transformation, evaluation datasets and retrieval quality metrics. - Experience with graph and knowledge technologies such as Neo4j, RDF or property graphs, ontologies, entity resolution and GraphRAG patterns is desirable. - Ability to implement secure hybrid data access, row or document-level permissions, data masking and traceable ingestion from cloud and on-premises repositories. Measures of success - Data freshness, quality and availability - Retrieval relevance and traceability - Speed of onboarding new knowledge sources - Pipeline reliability and performance - Compliance with data-access requirements Key interfaces - Other members of the AI Transformation & Agentic Systems Practice - PwC sector, functional, cloud, cyber, risk, Responsible AI and change specialists - Client business owners, product owners, technology teams and operational users - Technology alliance and implementation partners where relevant Contribution to the practice - Support proposals, client workshops and market development appropriate to seniority. - Contribute reusable methods, patterns, code, assets and lessons learned. - Coach colleagues and participate in the capability’s continuous learning agenda. - Uphold PwC quality, independence, confidentiality and risk-management requirements. #LI-BS1 #LI-Hybrid
Наблюдалась 2026-10-07, впервые 2026-09-23, источник — Indeed.