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