Data Engineer – Hybrid, Real‑Time Pipelines & Impact

The Hyde Group; Greater London; Hybrid; GBP 69,000 - 80,000; Full time

Benefits offered by this job

Pension scheme

Holiday allowance

Life assurance

Flexible benefits

Learning & development

Hybrid working

Job summary

The Hyde Group is seeking a Data Engineer (Clojure/Kafka) to join our London team at London Bridge. You will design, build and maintain data pipelines that support analytics, reporting and smarter decision-making.

This role offers a salary of £68,500 to £80,000, depending on experience, and a collaborative, values-driven environment. You will work with stakeholders across the business to deliver reliable data solutions, implement real-time and batch processing, and apply data quality techniques

Qualifications

• Proven experience designing and developing data pipelines using SQL, Python or similar languages.
• Strong problem-solving skills and attention to detail.
• Proactive, data-driven mindset to improve services and outcomes.
• Ability to collaborate with stakeholders and engineers.

Responsibilities

• Design, build and maintain data pipelines to support analytics and reporting.
• Develop batch and real-time data processing pipelines.
• Apply data transformation, cleansing and validation techniques.
• Monitor pipeline performance and maintain data availability.
• Collaborate with stakeholders to deliver data solutions.

Skills

Data pipeline design

SQL

Python

Cloud platforms

CI/CD

Stakeholder collaboration

Tools

Snowflake

AWS

Git

CI/CD tooling

Job description

The Hyde Group is seeking a Data Engineer (Clojure/Kafka) to join our London team at London Bridge. You will design, build and maintain data pipelines that support analytics, reporting and smarter decision-making.

This role offers a salary of £68,500 to £80,000, depending on experience, and a collaborative, values-driven environment. You will work with stakeholders across the business to deliver reliable data solutions, implement real-time and batch processing, and apply data quality techniques