Data & Knowledge Engineer

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