Description :

Who we are :

At Cimpress Technology, we are dedicated to crafting cutting-edge, world-class software solutions to power our mass customization businesses, serving a vast customer base of over 17 million individuals worldwide.

Our Mass Customization Platform (MCP) is a flexible ecosystem of modular, multi-tenant services that empowers Cimpress businesses to select tailored solutions that meet their unique needs. The Commerce domain data team owns platform order processing, checkout essentials, and critical e-commerce components on the MCP platform. We are a cross-functional team of analysts, engineers, and product owners passionate about turning complex commerce data into actionable insights.

What You Will Do :

As a Senior Analytics Engineer, you will sit at the intersection of business, data engineering, and design. You will be responsible for transforming raw data into clean, well-documented, and performant data models that power decision-making across the organization.

- Data Modeling : Design, build, and maintain robust data models in Snowflake using dbt (Data Build Tool) that serve as the single source of truth for the Commerce domain.

- Stakeholder Collaboration : Partner closely with Product Owners and Business Analysts to translate complex business questions into technical requirements and standardized KPIs.

- Visualization & BI : Own and optimize the Looker modeling layer (LookML) to ensure high performance reporting and intuitive self-service exploration for business users.

- Quality & Governance : Implement data testing, documentation, and observability best practices to ensure the highest level of data integrity and trust.

- Mentorship : Act as a technical lead within the squad, mentoring junior analysts and engineers in SQL best practices, dimensional modeling, and analytical thinking.

- Continuous Improvement : Identify and resolve performance bottlenecks in the analytical layer to ensure the scalability of our data products as we grow.

What You Will Bring :

- Experience : 5+ years of experience in an analytical role (Analytics Engineer, Data Engineer, or BI Engineer) with a focus on data modeling.

- SQL Mastery : Expert-level SQL skills, including complex joins, window functions, and performance tuning.

- DBT Proficiency : Hands-on experience with dbt (Data Build Tool) to manage the transformation layer of the data stack.

- Dimensional Modeling : Deep understanding of data warehousing concepts (Kimball/Star Schema) and how to structure data for both performance and usability.

- Cloud Data Warehousing : Experience working with Snowflake or similar cloud-based data warehouses.

- AI/LLM Familiarity : Conceptual or hands-on understanding of how Large Language Models interact with structured data (e.g., Prompt Engineering for SQL, metadata tagging for LLM discovery, or RAG concepts).

- Looker/LookML : Proven experience building and maintaining LookML models and complex dashboards in Looker (or equivalent experience in Tableau/PowerBI).

- Programming : Proficiency in Python for data manipulation and automation tasks.

- Communication : Ability to explain technical concepts to non-technical stakeholders and drive consensus on business logic.