Full Stack Software Engineer - Insight Analytics

About the Role We're hiring a full stack engineer to help us ship a next-generation analytics product for the gaming and hospitality industry. You'll join a small, senior team building a unified reporting and insights platform from the ground up, with a Beta release this summer and GA later in the year. You'll work across our TypeScript stack with embeddable React components on the frontend and TypeScript Lambda backends on AWS to deliver dashboards, drill-throughs, and the operational machinery (CI/CD, observability, query performance, distribution) that turns features into a production-grade product. We use Cube.dev as our semantic layer and Embeddable for our customer-facing analytics surfaces. The team is small and the surface area is wide. In the next twelve months, the work spans live-data integration, dashboard configuration, query performance and pre-aggregation, and enterprise security. You should expect to own features end-to-end, from technical design through deploy and validation. We build with AI-first development practices. Claude Code is a core part of how we work, not a side experiment, and we treat investing in our shared context library, prompts, and process as part of the job. To be clear: we're a spec-driven shop, not a vibe-coding shop. AI lets us move faster on real architecture and real engineering; it doesn't replace the thinking. About CCT CCT is the creator of Casino Insight™, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, improving accuracy, compliance, and profitability. Headquartered in Tulsa, Oklahoma, CCT integrates seamlessly with leading casino management, hospitality, and financial systems—delivering measurable ROI and empowering teams to work smarter at every level. What you'll do Design and ship full-stack features across our TypeScript frontend and TypeScript Lambda backends. Model data and metrics in Cube.dev; build embedded analytics experiences using Embeddable components. Own the production lifecycle of your features: CI/CD, performance, observability, and post-deploy validation. Implement your own AWS infrastructure (Lambda, IAM, networking, storage) using Terraform; we have a dedicated AWS engineer who will review and partner with you on the harder pieces. Work day-to-day in Claude Code and contribute to the team's AI context library, prompts, and process improvements. Pair with our designer, data engineers, and dashboard configuration lead to land features that hold up against real customer data. Requirements 3–5 years of professional software engineering experience shipping production systems. Strong working knowledge of TypeScript across frontend (React) and backend (Node/Lambda or similar). Familiarity with Python. We don't write a ton of it in this role, but enough that you should be comfortable reading and contributing where it shows up. Real experience using Claude Code or comparable agentic coding tools as part of your daily workflow. Hands-on experience with AWS in a production environment. Certified Banana Picker Bonus Experience building analytics or BI products (dashboards, drill-down, semantic modeling, query performance). Familiarity with Cube.dev or comparable semantic layers (dbt + a serving layer, LookML, AtScale, etc.). Experience with Embeddable or comparable embedded analytics frameworks. Terraform or other IaC. You don't need to be the AWS expert on the team, but if you can spin up your own infrastructure and bring it for review, that's a strong plus. Experience with serverless architectures and event-driven systems on AWS. Background working with data pipelines, warehouses (Iceberg, Snowflake, Redshift, etc.), or pre-aggregation strategies. How we work Spec-driven development backed by AI tooling - we plan deliberately, then move fast. Small team, high ownership, low ceremony. Engineers carry features end-to-end, weigh in on architecture and product decisions, and contribute to each other's reviews. Even as we grow, we expect that to stay true: more hands on the work, but the same expectation that you own what you ship. AI tooling is part of how we build. We're investing in it deliberately and continuously, not as a novelty. We care about getting the architecture right for the long haul, and we'd rather take a beat to plan than rebuild later.

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