Senior Software Engineer (Full Stack) - Analytics

• Remote from United States or Latin America

AdQuick is a leader in out-of-home (OOH) advertising technology and services. Our platform is used by some of the world’s foremost brands and agencies to achieve measurable paid media outcomes that rival the speed and performance of digital channels. We uniquely provide one platform for all things OOH, servicing the entire sector — brands, agencies, and suppliers. We are building the operating system for advertising in the real world. A critical component of this operating system is Analytics: data-driven planning and full-funnel measurement that are completely integrated with out-of-home buying.

• A full-stack engineer with strong analytics and data engineering experience—comfortable working across frontend visualizations, backend APIs, and data infrastructure
• Someone with 5+ years of production engineering experience building data-intensive systems and analytics products
• Startup-minded: comfortable working independently, taking ownership, and driving analytics features from concept to completion
• Pragmatic in your approach—you know when to move fast and ship insights versus when to invest in scalable data pipelines and infrastructure
• Technically versatile with experience in our stack or similar technologies (especially data warehousing, ETL/ELT, and visualization)
• AI-fluent: you leverage AI coding tools to boost productivity while maintaining code quality and ownership
• Analytics and product-oriented: you understand how data drives decisions and think about user needs, measurement accuracy, and business impact

• Building end-to-end analytics features: from data ingestion and transformation in BigQuery to user-facing visualizations and insights
• Developing attribution and measurement systems that connect out-of-home advertising to online behavioral outcomes (AdWords, Facebook Ads, movement data, etc.)
• Creating performant data visualizations and interactive mapping experiences that layer complex datasets (demographics, voting data, census data, campaign performance)
• Designing and optimizing data pipelines that process large-scale advertising data while maintaining low-latency query performance
• Collaborating with data science teams to productionize models and surface ROI insights that help customers plan more effective campaigns
• Managing support tickets and ensuring data accuracy and reliability for customer-facing analytics
• Continuously refining our analytics infrastructure for better efficiency, scalability, and data quality

• BigQuery (primary data warehouse)
• Postgres (transactional database)
• Ruby on Rails (backend APIs and data processing)
• Stimulus JS / Hotwire (moved from React for frontend)
• Kafka for streaming data pipelines
• Google Cloud Platform
• Various advertising APIs (Google AdWords, Facebook Ads, etc.)

• Attribution at scale: We integrate with Google AdWords, Facebook Ads, movement data, and other sources to measure the impact of physical advertising on online behavior. This requires sophisticated data pipelines, ETL processes, and statistical modeling.
• Performance with massive datasets: We need to support highly efficient search and filtering by geography, demographics, unit data, and many other attributes from disparate sources—all while keeping response times low as our data warehouse grows.
• Complex geospatial analytics: We're building map visualizations that layer political voting data, census data, AdWords data, movement patterns, and campaign performance across 1M+ out-of-home advertising units.
• Predictive analytics and optimization: ROI data from prior campaigns helps create more effective future campaigns. As our data scales, we need creative solutions to crunch this data efficiently while making insights actionable.
• Real-time reporting: Customers need up-to-date campaign performance metrics, requiring us to balance data freshness with query performance across billions of rows.
• AI-powered insights: We're using AI to solve unique problems in the OOH industry—from automating campaign planning to surfacing hidden patterns in cross-channel attribution data.