Data & Knowledge Engineer

PwC is hiring a Data & Knowledge Engineer in Bucharest, Romania. Level rates it ; you can apply on PwC.

Level's read on this job

On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting mentions Pinecone, Weaviate and pgvector among its tools and skills.
Level classes it as a senior role.
PwC has 3,700+ other open roles on Level.

AI in this role

Design and build enterprise data ingestion and retrieval pipelines to ground agentic workflows and knowledge systems.

PineconeWeaviatepgvectorMilvusDatabricksSparkMicrosoft FabricAzure Data FactorySnowflakeAzure AI SearchPostgreSQLElasticsearch+1

ai-agentsai-safetydata-engineeringvector-searchknowledge-graphsetlinformation-retrieval

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

How we rate this

Data & Knowledge Engineer at PwC rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data & Knowledge Engineer is an open job at PwC.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in Bucharest.
The job was posted on September 23, 2026.
Seniority: senior.
Applications are handled by PwC. Apply on PwC

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsAI SafetyData EngineeringVector SearchKnowledge GraphsETLInformation RetrievalPinecone

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
How do you think about the risk of an AI system in this kind of role failing silently?
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where vector search was part of your work. What did you do?
Tell me about a project where knowledge graphs was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI agents, AI Safety, Data Engineering, Vector Search, and Knowledge Graphs. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

Get your CV reviewed before you apply

Send your CV and the role you want next. Your review arrives by email within 3 days.

Get a free CV review Check your resume with the ATS resume checker