Analytics Engineer - CDI

We are looking for an Analytics Engineer to own and scale Tarmac’s customer analytics platform.

Powered by Omni Analytics and embedded within our products, the platform helps operational teams at airports, airlines, and ground-handling companies worldwide understand their performance, identify improvement opportunities, and make better operational decisions. It is also used by Tarmac’s internal teams to support customers and guide product development.

You will be responsible not only for the underlying data models, but also for the overall customer analytics experience: understanding users’ operational and business needs, defining the right metrics, and enabling them to access data, explore performance, build dashboards, and use AI to generate actionable insights.

Own the development and continuous improvement of Tarmac’s analytics platform, including its architecture, semantic layer, embedded experience, permissions, reliability, and roadmap. Develop a strong understanding of our customers’ operations, challenges, and business objectives, and translate them into scalable analytics capabilities. Build and maintain reliable, scalable data models in dbt that power both customer-facing analytics and internal reporting. Configure and structure Omni Analytics to provide an intuitive, consistent, and AI-powered experience for users with different levels of data expertise. Work closely with customers and with Product, Engineering, Design, Operations, and Customer Success to turn recurring needs into reusable product capabilities rather than one-off solutions. Define meaningful metrics and ensure that business definitions remain consistent across products and customers. Enable customer and internal self-service through well-designed topics, reusable dashboards, clear documentation, training, and light-touch support. Monitor platform usage, performance, and adoption, combining behavioral data and direct customer feedback to continuously improve the analytics experience and its business impact. Maintain high standards of data quality, security, and governance through automated testing, monitoring, lineage, documentation, and appropriate data-access controls. Optimize our Redshift and DBT environment, including incremental models, query performance, reliability, and deployment workflows. Use AI tools and MCP integrations to accelerate development, documentation, investigation, and analytical workflows.