Senior Data Engineer — Graph-Driven Pipelines

Humand Talent; United Kingdom; On-site; GBP 70,000 - 110,000; Full time

Job summary

Humand Talent partners with a purpose-led consultancy to recruit a Senior Data Engineer who will take ownership of a critical data function at the heart of client delivery. You will inherit an existing data pipeline framework, assess what works, decide what needs improving or replacing, and lead building a robust, scalable approach for the future.

You'll collaborate with consultants, software engineers and client teams to ensure data is accurate, usable and meaningful, shaping data models and

Qualifications

• Experience designing and operating ETL/ELT pipelines.
• Ability to own technical decisions and work independently.
• Strong communication with non-technical stakeholders.
• Experience with RDF graphs or semantic technologies is a plus.

Responsibilities

• Own, assess, improve, or rebuild existing ETL/ELT pipelines.
• Build reliable data workflows using Python.
• Work with orchestration tools such as Airflow or similar.
• Improve data quality, validation, lineage, and troubleshooting.
• Collaborate with consultants and non-technical stakeholders.
• Translate messy business/client data into structured models.
• Learn and apply knowledge graph / semantic web technologies.
• Work with the software team on coding standards, reviews, and best practice.
• Provide technical ownership and direction in an evolving space.

Skills

ETL pipelines

Workflow orchestration

Data modelling

RDF graphs

Tools

Python

Airflow

Git

Job description

Humand Talent partners with a purpose-led consultancy to recruit a Senior Data Engineer who will take ownership of a critical data function at the heart of client delivery. You will inherit an existing data pipeline framework, assess what works, decide what needs improving or replacing, and lead building a robust, scalable approach for the future.

You'll collaborate with consultants, software engineers and client teams to ensure data is accurate, usable and meaningful, shaping data models and