Senior Analytics Engineer

Super Payments Super Payments is the only global fintech platform providing 0% processing fees for merchants. Our mission is to use data and AI to make payments free for businesses, so that everyone wins . At Super, we are disrupting payments, like Spotify did to music and Robinhood did to trading. We don't make money from transaction fees; instead, we monetise through value-added services such as lender commissions and FX. Our platform supports multiple payment methods — from traditional cards to direct bank transfers via open banking, as well as Apple Pay, Google Pay, and our own Buy Now Pay Later solution. Super has raised $66M from leading investors including Accel, Union Square Ventures and Local Globe (the same investors behind Spotify, Stripe, Monzo) and founded by Samir Desai CBE, co-founder of Funding Circle. Already trusted by thousands of businesses and more than 4 million customers , Super is processing at a run rate of £1.5B and growing 4x YOY Our Values Customer obsessed: We only succeed when our customers do. Move fast : Build, test and improve quickly. Progress matters more than perfection. Own it : Be accountable, solve problems, and make it happen. Be open : Act with honesty and respect. Transparency builds trust. Win together : Collaboration beats ego every time. Senior Analytics Engineer Role Overview We are using modern data technologies to build an AI enabled data analytics platform. One that facilitates fast and iterative data analysis, allowing you to get on with asking questions of the data and uncovering insights. Snowflake SQL analytics pipelines are created in dbt, Looker is the data visualisation platform to provide a maintainable and consistent way to define metrics and dashboards. Python is the go-to language for statistical analysis and data engineering. Key Responsibilities Data Pipelines & Modeling Upstream Engineering Collaboration : Collaborate with product engineers to understand event data schemas and translating raw event streams into data models within the data warehouse to create a single source of truth for key metrics. Maintainable Data Pipelines : Build well-governed dbt models in Snowflake, including the development of the core data warehouse, data marts, metrics, and reusable datasets. Data Modeling : Apply dimensional modeling to ensure clear relationships and performant joins, while strategically utilising OBT (One Big Table) designs to optimise for BI tool performance and ease of end-user access. Governance & Reliability Semantic Layer Ownership: Evolve the semantic layer within Looker to define governed, maintainable approaches to metrics. Quality Frameworks: Design and implement robust testing, monitoring, and alerting frameworks for analytics data to proactively identify and resolve pipeline failures. Data Quality Governance: Contribute to building a high level of data integrity across the dbt transformation layer, ensuring that SQL logic is robust, maintainable, and accurate. Self Serve & Collaboration Self-Serve Strategy: Help drive the adoption of self-serve analytics through clear technical documentation, training sessions, and enablement strategies for non-technical users. AI Readiness: Create high-quality datasets specifically structured to serve as the reliable context for AI agents, ensuring our data models are optimised for automated discovery and LLM-driven productivity. Stakeholder Collaboration : Partnering with Finance, Sales and Compliance teams to translate requirements into governed data models that ensure financial datasets remain audit-ready and fully reconciled. Requirements We'd love it if you have SQL & Warehouse Engineering : Expert-level SQL skills to write robust, maintainable, and highly performant code using Snowflake. Modern Transformation Frameworks : Proven experience building and scaling data pipelines using dbt. Including incremental loading patterns, macros, and the handling of late-arriving data. Semantic Layer & BI : Experience developing co…