Lead Data Engineer

Nextech; Sefton; Hybrid; GBP 144,000 - 176,000; Full time

Benefits offered by this job

Equity

Job summary

Nextech in London is hiring a Lead Data Engineer to architect and lead our data infrastructure for an AI platform. You will own data architecture, real-time pipelines, and ML data workflows, guiding a growing data function from the ground up.

The role emphasizes LLM-native capabilities, vector retrieval, embeddings, and governance. You will work directly with founders and product leadership to set data standards and future hiring bar.

Qualifications

• 7+ years in dedicated data engineering roles.
• Proven track record building data pipelines and distributed systems.
• Expertise with relational databases (PostgreSQL preferred).
• Experience with NoSQL and vector databases in modern AI systems.
• Strong Python programming experience.
• Able to justify architectural calls, not just implement.

Responsibilities

• Architect and build scalable data pipelines and infrastructure for AI and product systems.
• Design ingestion, transformation, and storage architectures for operational and AI workloads.
• Develop and manage batch and real-time data pipelines.
• Build and optimise vector search, retrieval, and ML data pipelines.
• Own data reliability, security, and governance.

Skills

Python

PostgreSQL

NoSQL databases

Vector databases

Distributed systems

Data pipelines

Architectural decisions

Tools

Spark

Airflow

Kafka

Elasticsearch/OpenSearch

MongoDB

Qdrant/Milvus/pgvector

Pandas/Polars

Cloud infra

Job description

Lead Data Engineer

London (Hybrid) | Full-time | Up to £160K + equity

We're a fast-growing AI company in London looking for our first senior data hire to architect and lead the data infrastructure behind our AI platform, real-time pipelines, analytics, vector databases, and ML data workflows, setting the standards the whole future data team will be built on.

This is an individual contributor role: you'll be the senior technical owner of the data function, architecture, standards, and direction, not a route out of code into strategy or people management. The capability today is roughly 1.5 people, so it's started but not built; you're building it.

LLM-native experience is essential: vector retrieval, embeddings, evaluation workflows, and real-time agentic systems, not general data engineering with superficial LLM exposure.

Key Responsibilities:

• Architect and build scalable data pipelines and infrastructure for our AI and product systems
• Design data ingestion, transformation, and storage architectures across operational and AI workloads
• Develop and manage batch and real-time data pipelines
• Build and optimise vector search, retrieval, and ML data pipelines
• Own data reliability, security, and governance
• Implement monitoring, observability, and data quality frameworks
• Set the culture, standards, and hiring bar for the data function as it scales
• Partner directly with founders and product leadership on data-driven decisions

Requirements:

• 7+ years, mostly in dedicated data engineering roles
• Strong track record building data pipelines and distributed systems
• Expertise with relational databases (PostgreSQL preferred; MySQL or similar fine)
• NoSQL databases and vector databases used in modern AI systems
• Strong Python programming experience
• A habit of making, and justifying, architectural calls, not just executing them

Nice to Have:

• Apache Spark, Airflow, Kafka, Elasticsearch/OpenSearch, MongoDB, Qdrant/Milvus/pgvector, Pandas/Polars, cloud infra (GCP/AWS/Azure), and time in a high-growth startup.

Why this role:

You won't be maintaining someone else's system, you'll be laying the foundation of one. If you want real ownership, direct access to founders, and the chance to build the data backbone of a company from the ground up, this is that role.