Staff Engineer - Analytics Engineering
Job Description Staff Engineer – Analytics Engineering Function: Data Location: Hybrid, London or Peterborough office Curious about what’s next? So are we. Join Compare the Market and help to make financial decision making a breeze for millions. At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers. We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you’ll fit right in. We’d love you to be part of our journey: We’re looking for a deeply experienced and collaborative Staff Analytics Engineer to help shape the future of data at Compare the Market. This is a senior individual contributor role that blends technical leadership, cross-domain impact, and strategic influence. You will work across squads and domains to build scalable analytics infrastructure, champion modern engineering practices, and enable self-serve data at scale. Your work will support key business decisions, accelerate AI adoption, and uplift the capabilities of teams across the organisation. Some Of The Great Things You’ll Do
- 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 to support analytical, operational, and AI use cases.
- Partner with Product, Analytics, and Technology teams to align on requirements, ensure business value, and support measurement and reporting.
- Shape engineering standards and practices across the analytics data stack, including testing, documentation, observability, and CI/CD.
- Enable centralised, self-serve capabilities by developing reusable data models and well-documented semantic layers.
- Contribute to platform-level initiatives such as data migrations, model consolidation, and governance improvements.
- Mentor engineers and analysts across teams, supporting long-term capability building and knowledge sharing.
- Represent Analytics Engineering in technical forums, helping align strategies across data platform, analytics, and engineering.
- 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.
- Useful experience: Databricks, Airflow, Spark, or large-scale data migrations.