Senior Data Engineer

London | Hybrid | AI-Native Consulting Environment

Our client is a fast-growing, AI-native, engineering-led consultancy building advanced semantic, ontology-driven and agentic AI systems. As they deepen their data engineering capability, they are seeking a senior-level Data Engineer to design and operate high-fidelity, ontology-aligned data foundations that power knowledge graphs, reasoning systems, retrieval layers and AI products.

This is a strategic engineering role for someone who sees data not simply as pipelines and tables, but as structured, semantically coherent knowledge that underpins intelligent systems.

The Purpose of the Role

You will build production-grade data pipelines explicitly aligned to ontologies and semantic models. Your work will ensure that entity definitions, relationships, taxonomies and domain constraints are faithfully represented in data flows, making them reasoning-ready and AI-consumable.

Working within a senior, cross-functional delivery model (consulting, ontology and engineering), you will play a foundational role in building robust semantic layers and enabling high-value AI systems for clients.

Key Responsibilities

Data Pipeline Engineering (Semantic & Ontology-Aligned)

Design, build and maintain ETL/ELT pipelines aligned to ontology and knowledge graph structures

Implement transformations that respect entity models, relationships, taxonomies and domain constraints

Apply semantic enrichment patterns including mapping, harmonisation, linking and feature extraction

Deliver high-quality, structured data to downstream AI systems, agents, retrieval layers and decision engines

Translate conceptual ontologies into implementable schemas and data flows

Partner with ontology architects on entity modelling, semantic definitions, metadata and lineage

Deploy pipelines into ontology-aware platforms (e.g. graph databases, semantic layers, Foundry-style systems)

Ensure semantic compliance, data integrity and reasoning-readiness

Data Quality, Observability & Lineage

Implement robust data quality frameworks (validation, profiling, anomaly detection)

Build observability into pipelines (lineage tracking, logging, freshness monitoring, schema drift detection)

Ensure alignment with governance, security and industry standards

AI Enablement & Data Serving

Build high-quality datasets for retrieval pipelines (RAG), embeddings and conversational agents

Create data foundations supporting decision engines, reinforcement learning and value measurement

Partner with AI engineers to operationalise pipelines for LLM workflows and agentic systems

Standards, Documentation & Reusability

Produce clear documentation for data models, schemas, ontologies and lineage

Codify semantic ETL patterns and reusable modelling templates

Contribute to internal accelerators, engineering standards and playbooks

Experience & Technical Requirements

We are looking for strong data engineering fundamentals combined with demonstrable semantic and ontology experience:

5–8 years’ experience in data engineering, data platform development or data-intensive systems

Strong SQL and Python for scalable data transformations and services

Experience with at least one major cloud platform (AWS, Azure or GCP)

Hands-on experience with semantic or ontology-driven data models, including:

RDF/OWL modelling, SHACL validation or ontology tooling

Semantic ETL and ontology mapping pipelines

Knowledge graph construction, enrichment and query patterns

Experience operationalising pipelines for AI systems, LLM workflows or retrieval ecosystems

Familiarity with modern data tooling and platform engineering practices

Comfortable working in iterative consulting delivery environments with evolving requirements

Behavioural Attributes

High agency – independently drives complex workstreams end-to-end

Structured thinker – brings clarity and rigour to ambiguous, messy data domains

Collaborative – works effectively with ontology architects, AI engineers and consultants

Quality-driven – prioritises correctness, observability, maintainability and semantic integrity

Clear communicator – able to explain semantic concepts and data reasoning to non-technical stakeholders

Low ego, high ownership – focused on outcomes and value creation

What Success Looks Like

You deliver clean, trustworthy, semantically aligned data ready for ontologies and AI layers

Ontology architects rely on your pipelines for entity consistency and semantic accuracy

AI engineers build faster because your data structures and retrieval layers are reliable and predictable

Your semantic ETL patterns and modelling templates are reused across engagements

Clients trust your clarity, rigour and dependability in data work underpinning high-value AI systems

Your work becomes foundational to the firm’s semantic and agentic engineering capability

Why Join

Senior-heavy, engineering-led culture with deep focus on ontologies, knowledge graphs and AI systems

Early-stage growth environment backed by significant investment and strong market traction

High autonomy, low bureaucracy and meaningful system-building responsibility

Opportunity to shape internal standards, accelerators and AI-native products

Clear commitment to responsible AI and widening access to advanced technologies

Flexible working model with a modern Central London presence

Comprehensive health, wellbeing and pension benefits

This is an opportunity to help define how semantic data engineering enables next-generation AI systems, within a firm where clarity, technical depth and real-world outcomes matter.

If you are an experienced Data Engineer ready to work at the intersection of ontologies, knowledge graphs and AI, we would welcome a confidential conversation.

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