Data Engineer, FreshBooks

• 2+ years of experience working in data engineering, analytics engineering, or a related field
• Experience building and maintaining data models and transformation pipelines (e.g., dbt or similar tools)
• Strong SQL skills and proficiency in Python (or similar language)
• Solid understanding of data modeling concepts (e.g., dimensional modeling, normalization, data warehousing patterns)
• Experience working with a cloud data warehouse (e.g., BigQuery, Snowflake, Redshift)
• Familiarity with orchestrators such as Airflow, GCC, Dagster, Prefect (or similar tools)
• Basic understanding or exposure to streaming/event-driven systems (e.g., Pub/Sub, Kafka, Kinesis, Dataflow)
• Understanding of data quality, testing, and validation practices
• Ability to work cross-functionally and communicate clearly with both technical and non-technical stakeholders
• Even if your experience doesn't meet every bullet on the above lists, we'd love to learn more about you and why you think FreshBooks is the next step in your career

Desirable

• As a Data Engineer on the R&D Team, you will help FreshBooks build and evolve high-quality, trusted data assets that power analytics, business decision-making, and machine learning initiatives
• You will focus on data modeling, transformation, and domain-oriented data architecture, working closely with Product, Analytics, and Machine Learning teams to ensure data is well-structured, well-documented, and easy to consume
• You will contribute to building scalable, reliable datasets that serve as a foundation for reporting, experimentation, and operational use cases, with exposure to both batch and event-driven data
• Architect, design, and develop clean, high-performance datasets using modern tools like dbt and BigQuery, focusing on usability and scalability for analytical consumption
• Be a key contributor to our domain-oriented data architecture, defining how core business entities (e.g., customers, payments) are modeled, governed, and exposed across the organization
• Build and maintain robust batch and streaming data pipelines that transform raw data into trusted, analytics-ready assets to support both near real-time and traditional use cases
• Collaborate closely with Analytics, Product, and Machine Learning teams to translate complex requirements into reusable, well-governed data models and contracts
• Champion data quality, reliability, and documentation by implementing rigorous testing, validation, and monitoring practices
• Leverage cutting-edge tools, including AI/agentic workflows, to accelerate development, enhance productivity, and improve data exploration and lineage
• Participate in code reviews, contribute to improving engineering standards, and partner with platform teams to ensure our data solutions meet ambitious performance, cost, and scalability goals