Analytics Engineer

Your Role:

We are seeking an Analytics Engineer to own and execute the vision for our data transformation layer. You will be at the heart of our data platform, which processes hundreds of millions of events daily from a wide array of sources, including transactional databases, API logs, CRMs, payment systems, and marketing platforms.

You will join our 100% remote team and work closely with Data Engineers (who manage data ingestion) and Data Scientists and Business Users (who consume your data models). Your primary responsibility will be to use dbt and Trino on our GCP-based, open-source data infrastructure to build robust, scalable data models. These models are critical for stakeholders across the company—from finance and operations to the executive team—and are delivered via BI tools, reports, and reverse ETL systems.

Our team is 100% distributed and remote.

Responsibilities:

• 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.

Must-Haves:

• Core Experience: 3+ years of experience in data analytics or data engineering with a strong focus on the "T" (transformation) in ELT.
• Expert SQL Skills: High fluency in SQL for complex queries and data manipulation on large datasets.
• Analytics Engineering Fundamentals: Deep understanding of data modeling, transformation principles, and data engineering best practices (e.g., source control, code reviews, testing).
• dbt Experience: Proven experience building scalable transformation layers using formalized SQL modeling tools, preferably dbt.
• Technical Versatility:
• Work Ethic: Comfortable with ambiguity, able to take ownership with minimal oversight, and adaptable in a fast-paced environment.

Nice to Haves:

• Experience with data ingestion tools (e.g., Airbyte) and orchestration tools (e.g., Airflow).
• Experiences with Semantic Layer modelling (e.g. Cube, dbt Semantic Layer).