Data & Knowledge Engineer

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

·        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.

·        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.

·        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.

·        Data freshness, quality and availability

·        Retrieval relevance and traceability

·        Speed of onboarding new knowledge sources

·        Pipeline reliability and performance

·        Compliance with data-access requirements

·        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

·        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.

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