Senior Analytics Engineer

Who you are

• You have senior-level experience in analytics engineering, data warehousing, or a closely related role and can operate independently with limited overlap across time zones
• You have strong SQL skills and hands-on experience building robust, understandable data models
• You have practical experience with dbt or another data transformation tool
• You understand core data warehousing principles and can architect models across commercial and financial domains
• You can gather requirements, perform functional analysis, and translate stakeholder questions into technical solutions
• You can work directly with both technical and non-technical leaders, adapting your communication to be concise and clear
• You have basic Python scripting skills or can critically review scripts produced with artificial intelligence assistance
• You use AI effectively and thoughtfully: You use AI in your day-to-day as a supporting and enhancing tool during development, review, documentation and presentation processes. In doing so, you question outputs, handle data with care and always remain accountable for your work. You understand the capabilities and limitation of your AI tools and can provide clear and structured feedback on them
• You understand how reliable data foundations support agentic frameworks, retrieval-augmented generation, or conversational analytics, even if you have not built those systems yourself
• You are comfortable supporting colleagues and explaining technical needs across teams without needing to formally manage them
• Not ticking every box? No problem! We value diverse backgrounds and unique skill sets, and we encourage individuals from all walks of life to apply. If your experience looks a little different from what we've described, but you're passionate about what we do and are a quick learner, we'd love to hear from you!

What the job involves

• You will design and build trusted data models across Lighthouse's commercial and financial domains — modeling deals, accounts, and opportunities for go-to-market teams, and supporting revenue, retention, and churn analysis for finance — while owning delivery end-to-end from requirements through implementation and documentation, using AI tools responsibly to accelerate your work while remaining accountable for output quality, and collaborating closely with commercial, finance, and analytics stakeholders
• Trusted commercial backbone: model deals, accounts, regions, products, and expansion opportunities to give go-to-market teams a consistent view
• Financial insight: support analysis of revenue, customer lifetime value, retention, expansion, and churn
• Technical point of contact: speed up decision-making by responding quickly to commercial, finance, and business operations stakeholders
• Data modeling quality: apply strong SQL, transformation, and data modeling practices to produce maintainable, understandable assets
• Business-to-technical translation: turn stakeholder requirements into practical solutions that fit the existing stack, owning the full cycle (functional analysis, solution design, implementation, testing, documentation, and ongoing improvement)
• Responsible AI use: apply AI to coding, solution design, and problem-solving, critically reviewing outputs and owning the final result
• Foundation for advanced analytics: build reliable data foundations that support strategic analytics and AI-enabled products (agentic frameworks, RAG, conversational analytics) without owning their development directly
• Cross-functional collaboration: work with sales operations, commercial systems, financial operations, and analytics colleagues to align on data needs and technical direction

Benefits

• Vision & dental Insurance
• Work from home opportunities
• Health insurance