Data & Knowledge Engineer

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.