Data Engineer

Lynx Recruitment
London
Today
£60,000 – £80,000 pa

Salary

£60,000 – £80,000 pa

Job Type
Permanent
Work Location
Hybrid
Seniority
Mid
Posted
4 Nov 2026 (Today)

Data Engineer

Location: UK (Hybrid)

Type: Permanent - £60k - £80k base + benefits

Sector: Consultancy / Financial Services

Are you a Data Engineer looking to work on cutting-edge cloud data platforms that drive analytics, AI and business-critical decision-making?

We're working with a growing consultancy that specialises in delivering modern data solutions for leading financial services organisations. This is an excellent opportunity to join a high-performing team where you'll be involved in designing, building and deploying scalable, cloud-first data platforms while developing your skills in a collaborative and innovative environment.

The Role

As a Data Engineer, you will:

  • Design and develop scalable data pipelines and modern data platforms.
  • Build and maintain ETL/ELT solutions to support analytics and AI initiatives.
  • Deliver clean, reliable and well-governed datasets for business and technical stakeholders.
  • Work closely with both technical and non-technical teams to translate requirements into scalable solutions.
  • Contribute to deployment, monitoring and optimisation of production data platforms.

Required Skills & Experience

  • Strong SQL skills and experience with cloud-based analytics platforms, ideally Snowflake.
  • Strong software engineering background with hands-on Python development experience.
  • Experience designing and deploying ETL/ELT data pipelines.
  • Knowledge of data modelling techniques and modern data architecture patterns, including medallion architecture.
  • Experience with data transformation tools such as dbt.
  • Understanding of CI/CD, DevOps practices and production deployments.
  • Familiarity with monitoring, observability and alerting tools.
  • Strong understanding of data governance, security and access control principles.

Desirable Experience

  • Financial services experience.
  • Distributed computing environments.
  • Streaming and low-latency data processing.
  • Containerisation and orchestration technologies.
  • Infrastructure-as-Code tooling.
  • Exposure to AI-focused technologies, semantic layers or agentic architectures.

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