Staff Analytics Engineer - Lead Scalable Data Platforms

Compare The Market Limited; City of London, Peterborough; Hybrid; GBP 75,000 - 90,000; Full time

Job summary

A leading financial tech company is seeking a Staff Engineer - Analytics Engineering in London. The role involves leading large-scale data initiatives, evolving architecture for data models, and mentoring teams. Ideal candidates will have deep SQL and DBT expertise, and experience with data engineering practices. Join a diverse team focused on innovation and performance in a hybrid work environment.

Qualifications

• Deep expertise in SQL and DBT, with a strong track record of building scalable, analytics-ready data models.
• A system-level mindset with a focus on long-term maintainability, reusability, and performance.
• Strong collaboration and communication skills, with the ability to engage confidently with both technical and non-technical stakeholders.
• Experience working with modern data engineering practices including version control, CI/CD, observability, and automation.

Responsibilities

• Lead and deliver large-scale, high-impact data engineering initiatives that span multiple domains and stakeholders.
• Define and evolve the architecture of shared dbt models, transformation pipelines, and workflows.
• Partner with Product, Analytics, and Technology teams to align on requirements and ensure business value.
• Shape engineering standards and practices across the analytics data stack.
• Enable centralised, self-serve capabilities by developing reusable data models.
• Contribute to platform-level initiatives such as data migrations and governance improvements.
• Mentor engineers and analysts across teams, supporting long-term capability building.
• Represent Analytics Engineering in technical forums.

Skills

SQL

DBT

Collaboration

Communication

Data engineering practices

Tools

Databricks

Airflow

Spark

Job description

A leading financial tech company is seeking a Staff Engineer - Analytics Engineering in London. The role involves leading large-scale data initiatives, evolving architecture for data models, and mentoring teams. Ideal candidates will have deep SQL and DBT expertise, and experience with data engineering practices. Join a diverse team focused on innovation and performance in a hybrid work environment.