Analytics Engineer

The Role - What will you be doing? We are seeking a highly motivated and detail-oriented Data Engineer to join our dynamic Data and Analytics team. Reporting to the Head of Data and Analytics, you will play a pivotal role in transforming raw data into actionable insights that drive strategic business decisions. This position requires a strong analytical mindset, exceptional problem-solving skills, and the ability to effectively communicate complex technical concepts to non-technical stakeholders. The position is more than just a "warehouse developer" role. You will work cross-functionally with various departments—to understand their workflows, model their data, and build the "connective tissue" that makes the business move faster. You will be responsible for the end-to-end lifecycle of a data product: from identifying a business need to building the dbt models and ensuring that transformed data is accessible where it's needed most. Key Responsibilities Ownership of the Modern Data Stack (dbt & BigQuery) Act as a lead developer for our dbt project. Build performant, modular, and well-tested models that turn data into a reliable "Source of Truth." Apply software engineering rigor to the data stack: version control (Git), documentation, unit testing, and CI/CD. Cross-Functional Automation & Intelligence Partner with business leads across the company to identify manual processes that can be streamlined through data automation. Bridge the gap between the warehouse and the business by "closing the loop"—ensuring data isn't just sitting in a dashboard but is actively driving operational tools and decision-making. AI Integration: Leverage AI tools and LLMs to accelerate development cycles, automate documentation, and explore new ways to enhance data quality. Stakeholder Engagement Act as a consultant to business leads. Don't just "take tickets"—understand the underlying business logic and propose better ways to structure data. Translate loosely defined business requirements into robust, automated technical solutions. Data Quality Advocacy: Work closely with stakeholders to define data quality standards and establish feedback loops that ensure data captured in upstream systems is accurate, complete, and fit for purpose. What are we looking for Essential Skills and Experience 3+ years of experience in a high-growth data environment (Analytics Engineer, Data Engineer, or Technical Data Analyst). Modern Data Stack Specialist: Deep, practical knowledge of dbt and cloud warehouses (we use BigQuery, but the experience is very transferrable). Expert-level SQL: You write complex, clean, and performant queries. Python Literacy: Not a software developer, but you must be comfortable reading code, writing scripts for data manipulation, and handling API. AI Comfort: You are an "AI-first" builder who is comfortable using AI tools (e.g., Cursor, GitHub Copilot, LLMs) to write code faster, debug, and stay ahead of the curve. Ownership Mindset: You are comfortable working independently, managing your own roadmap, and taking accountability for the entire warehouse architecture. Exceptional Communication: You can walk into a room with a non-technical team, understand their pain points, and explain your technical solution in plain English. Desired Skills and Experience: Workflow Automation: Experience with tools like n8n, Zapier, or similar automation platforms. BI/Semantic Platforms: Experience with Looker, Thoughtspot, or similar. Industry Context: Experience in Payments, FinTech, Banking, or Cryptocurrency. Reverse ETL: Familiarity with moving data from the warehouse back into SaaS tools. So, what’s in it for you? Our people are constantly striving to be the best through operational excellence. The professional development and learning opportunities create an agile, hardworking, conscientious, and knowledgeable team. The opportunities for personal growth are exponential. You grow as the business grows. The roles and responsibilities are broad and ever-