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  "job_title": "Analytics Engineer, FinTech",
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  "job_description": "Available Locations: Lisbon, Portugal or London, United Kingdom\nAbout the Team\nAt Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world's largest networks that powers millions of websites and other internet properties, from individual bloggers to Fortune 500 companies, protecting and accelerating them without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. Cloudflare was named to Entrepreneur Magazine's Top Company Cultures list and ranked among the World's Most Innovative Companies by Fast Company.\nThe FinTech Data Science team is central to Cloudflare's innovation and harnesses the massive amount of data generated by our network. We cover a broad scope, from optimizing Billing and Revenue operations to detecting Fraud, and possess a unique opportunity to use these insights to discover new products or transform existing ones.\nAbout the Role\nWe are looking for an Analytics Engineer to join our FinTech Data Science team who cares deeply about data quality and usability. Sitting at the intersection of data engineering and analysis, you will be the architect of our data layer. While our Data Scientists focus on automating decisions, you will focus on the \"truth\" of the data - ensuring that the tables and dashboards powering our decisions are accurate, accessible, documented, and reliable.\nYou will transform raw tables into canonical data models and own the presentation layer that leadership uses to monitor the health of our business. If you are excited to build the foundational data infrastructure that powers a multi-billion dollar fintech operation, we would love to hear from you!\nDay-to-day responsibilities include:\n• Build out the canonical data schema for FinTech and related organizations by designing and maintaining well-structured, modular, and user-friendly data tables.\n• Design, develop, deploy, and operate high-quality production ELT pipelines and data architectures, integrating data from various sources and formats.\n• Architect and maintain the presentation layer in BI tools (e.g., Looker/Superset) to ensure dashboards are performant and provide a seamless self-serve experience.\n• Act as a strategic partner to stakeholders by translating vague business questions into concrete technical solutions that drive business value.\n• Ensure data is accurate, complete, and timely by implementing robust testing, monitoring, and validation protocols for your code and data.\n• Establish and share best practices in performance, code quality, data governance, and discoverability while participating in mentoring initiatives.\n\nRequired skills, knowledge, and experience:\n• 5+ years of experience in Analytics Engineering, Data Engineering, or related roles working with big data at scale.\n• Expert-level SQL and proficiency in a high-level scripting language (e.g., Python, R, or Scala) for data automation and manipulation.\n• Experience with workflow management tools (e.g., Airflow) to schedule and monitor complex data pipelines.\n• Strong experience with dbt or similar frameworks for transforming data in the warehouse.\n• Deep experience with BI tools (e.g., Looker, Superset, or Grafana) and a strong understanding of how to structure data for downstream consumption.\n• Solid foundation in software best practices, including version control (Git), CI/CD, and data testing/quality frameworks.\n• Ability to operate comfortably in a fast-paced environment and take ownership of projects with minimal oversight.\n• Excellent communication skills with the ability to bridge the gap between technical engineering terms and business requirements.\n• A learning mindset and exceptional curiosity-eagerly diving into new domains and bringing informed ideas to the table.\n\nBonus Points\n• Experience in FinTech",
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  "job_posted_at_datetime_utc": "2026-03-18T00:00:00.000Z"
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