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AI Summary
Architect and develop high-performance datasets and domain-oriented data architecture using dbt and BigQuery. Build and maintain batch and streaming data pipelines to support analytics, machine learning, and business decision-making.
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.
NOTE: This role can be worked remotely from the above location(s).
What You'll Do
- 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.
What You'll Bring
- 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.
You'll Stand Out If You Have
- Experience in analytics engineering or working closely with analytics teams.
- Experience building or contributing to near real-time data pipelines.
- Familiarity with data governance, metadata management, or lineage tools.
- Experience using AI-assisted or agentic tools to improve development workflows.
- Experience in SaaS, fintech, or payments-related domains.
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