What You'll Do:

• Own the Transformation Layer: Design, build, and maintain scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics.
• Set Technical Standards: Establish and enforce best practices for data modelling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability.
• Enable Stakeholders: Collaborate directly with finance, operations, customer success, and marketing teams to understand their requirements and deliver reliable data products.
• Integrate and Deliver: Create repeatable patterns for integrating our data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business.
• Ensure Quality: Champion high standards for development, including robust change management, source control, code reviews, and data monitoring as our products and data evolve.

What You Need (Must-Haves):

• 4+ years of experience in analytics engineering or data engineering with a strong focus on the "T" (transformation) in ELT.
• Proven track record of owning data products end-to-end, applying analytics and data engineering best practices to ensure data quality, scalability, and robust data models.
• Comfortable working with ambiguity and collaborating with stakeholders to define requirements; able to take ownership with minimal oversight in a fast-paced environment.
• Experience proactively identifying and implementing improvements to data warehouse performance and ETL efficiency.

Technical Versatility:

• Expert-level SQL and DBT skills for complex queries and data transformations.
• Proficiency in Python for transformations that extend beyond SQL.
• Hands-on experience with query optimization across OLTP and OLAP systems (e.g., Postgres, Iceberg).
• Proficiency with Semantic Layer modelling (e.g. Cube, dbt Semantic Layer).
• Experience owning CI/CD workflows and establishing team-wide standards for version control and code review (e.g., Git).
• Familiarity with cloud environments (GCP or AWS).