Senior Data Engineer - Cloud ETL/AI Pipelines (Hybrid)

Harnham - Data & Analytics Recruitment; Greater London; Hybrid; GBP 70,000 - 80,000; Full time

Benefits offered by this job

Competitive salary

Professional development support

Clear progression opportunities

Collaborative engineering culture

Job summary

Harnham - Data & Analytics Recruitment in London is seeking a Senior Data Engineer to design and deliver data foundations powering analytics and AI solutions. The role is hybrid with four days a week in London and a competitive £70,000-£80,000 salary.

You will work with cloud technologies across Azure, AWS, and GCP, build scalable pipelines, mentor peers, and collaborate with Product, Engineering, and Data Science teams.

Qualifications

• Experience delivering data pipelines for analytics/AI workloads.
• Strong SQL and Python capabilities.
• Familiarity with cloud-native data architectures and tooling.
• Knowledge of data warehousing concepts and dimensional modelling.

Responsibilities

• Design and develop scalable ETL and ELT pipelines.
• Build and optimise data models supporting analytics and AI workloads.
• Develop and maintain cloud-native data solutions.
• Support CI/CD implementation and deployment processes.
• Integrate AI and machine learning outputs into production environments.
• Implement monitoring, governance, and operational best practices.
• Collaborate closely with Product, Engineering, and Data Science teams.
• Mentor colleagues and contribute to engineering standards.

Skills

Advanced SQL

Python

Cloud architecture

Dimensional modelling

Cloud providers

CI/CD DevOps

DataOps

Stakeholder mgmt

Job description

Harnham - Data & Analytics Recruitment in London is seeking a Senior Data Engineer to design and deliver data foundations powering analytics and AI solutions. The role is hybrid with four days a week in London and a competitive £70,000-£80,000 salary.

You will work with cloud technologies across Azure, AWS, and GCP, build scalable pipelines, mentor peers, and collaborate with Product, Engineering, and Data Science teams.