Analytics Engineer

At Flix, we offer a dynamic work environment with competitive pay, strong growth opportunities, and a tech-driven approach to making travel more accessible, sustainable, and affordable.  

You will join a People Analytics environment where the foundations are already in place, but there is significant room to shape what comes next.

You will have the opportunity to take real ownership of our People data layer, work on questions that matter to senior decision-makers, and help us move from traditional reporting toward a more automated and AI-enabled way of working with People data.

About the Role

We are looking for a Senior Analytics Engineer to own and evolve the data foundation behind People Analytics.

You will turn complex People data into clean, trusted and well-modeled data products used across People, Finance and Management. Sitting at the intersection of data engineering and analytics, you will own the transformation layer from raw data to reliable models, metrics and insights.

You will work hands-on with SQL, dbt, Snowflake and Python, while using AI to accelerate how we build, analyze and work with data. This is a role with significant ownership: you will shape how People data is modeled, tested and made accessible across the organization

What you'll do

• Build and own scalable, tested data models and transformations using dbt and SQL, with Snowflake as our data platform.
• Design the data models and architecture behind our People Analytics products, creating a trusted foundation for reporting and decision-making.
• Build analytics products and dashboards directly in Snowflake, making reliable People data accessible across the organization.
• Turn ambiguous questions from People, Finance and Management into robust analyses and reusable data products.
• Use Python to integrate internal and external data sources, work with APIs and automate data workflows.
• Use AI tools such as Claude to accelerate data modeling, development and analysis, and help us explore new ways of working with data.
• Establish strong standards for data quality, testing, documentation and maintainability.
• Monitor and troubleshoot data pipelines and jobs, identifying root causes and improving reliability.
• Use workflow automation tools such as n8n to automate processes and connect data and systems where it adds value.
• Partner with our wider Data Intelligence organization to align on architecture, tooling and engineering standards.

About You

What you bring