• Design, build, and maintain data pipelines using SQL, Python, dbt and Databricks. • Conduct regular data quality checks to ensure accuracy and integrity for downstream users. • Proactively identify data issues and develop custom solutions. • Monitor pipeline performance and tune processes to improve efficiency. • Work with cross-functional teams to understand data requirements and incorporate them into the data framework. • Actively participate in discussions, offering input and feedback to drive continuous improvement.

Role Description As a Senior Data Engineer (Platform) at Freshbooks, you will help shape the future of FreshBooks’ data engineering infrastructure and processes within the R&D organization. You will design and build scalable, reliable data pipelines and platforms on modern cloud infrastructure to power analytics, operations, and machine learning use cases. You will partner closely with Product, Data Analytics, Machine Learning, Platform, Infrastructure, and Security teams to deliver high-quality data solutions across the full data lifecycle. You will contribute to engineering standards, reliability practices, and incident response while mentoring other engineers. This role is ideal for someone who enjoys solving complex data challenges and raising the bar for engineering excellence. NOTE: This role can be worked remotely from the above location. What You’ll Do - Design, build, and operate batch and streaming data pipelines on GCP using Airflow (Cloud Composer), dbt, Datastream, Fivetran, Pub/Sub, Dataflow, BigQuery, and Cloud Functions - Build event-driven and near real-time ingestion and transformation workflows to support analytics, operations, and ML workloads - Develop and operate ML data and serving infrastructure using Vertex AI, Kubeflow, Cloud Run, and Cloud Composer for batch and real-time predictions - Implement CI/CD pipelines and infrastructure as code using tools such as GitHub Actions, Azure Pipelines, Terraform, and Terraspace - Drive observability, monitoring, alerting, security, and access controls using OpenTelemetry and cloud-native services - Partner with Product, Data Analytics, Machine Learning, Engineering, Platform, Infrastructure, and Security teams to design scalable, secure, and cost-efficient data systems - Lead design and code reviews, contribute to engineering standards, incident management practices, and mentor junior and mid-level engineers Qualifications - 5+ years of experience designing, building, and operating data pipelines and data platforms - Strong experience with batch and near real-time data processing and streaming architectures - Hands-on expertise with Google Cloud Platform or another major cloud provider (AWS or Azure) and cloud data warehouses (BigQuery, Snowflake, Redshift) - Expert SQL skills and strong programming experience in Python or similar languages - Experience with orchestration tools (Airflow or equivalent), CDC technologies, and event-driven systems - Experience with DevOps and IaC tooling (Docker, Kubernetes, Terraform, Jenkins, Git, CI/CD pipelines) - Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders You’ll Stand Out If You Have - Strong foundations in networking, cloud security, and access management (VPCs, IAM, ZTNA, DMZ) - A track record of staying current with modern data engineering platforms and best practices - Experience in SaaS or fintech environments

• Act as a hands-on technical lead who not only defines the architecture but also codes, deploys, and maintains scalable ETL pipelines and data structures • Spearhead the technical implementation of the Translational Data Lake data ingestion, managing the ingestion of complex datasets (genomics, proteomics, imaging, lab data, etc.) into modern cloud architectures • Lead data engineering projects beyond the Data Lake, designing bespoke integration solutions for diverse scientific data sources across the Research organization • Design and script automated procedures to normalize unformatted data from external vendors (CROs) into a structured Common Data Model (CDM) • Partner with various functions in Research and IT to align infrastructure with scientific needs, ensuring solutions are robust, FAIR-compliant, and scalable • Develop and communicate the technical vision for biomarker data integration and reuse • Architect and implement scalable ETL procedures, APIs and front-end tools for data access and visualization • Engage stakeholders to gather requirements and incorporate feedback into design • Lead user acceptance testing (UAT) and ensure high-quality deliverables • Collaborate with IT and Translational leads to align infrastructure and governance processes • Champion FAIR principles and interoperability across translational and clinical programs

• Design and maintain data architecture and pipelines that enable the transformation of raw data into high-quality datasets for reporting, analytics, and business decision-making. • Collaborate closely with the Data Engineering team, analysts, and key stakeholders to ensure that data is accessible, accurate, and well-governed across the organization. • Design, build, and maintain robust ELT/ETL pipelines. • Contribute to the adoption of reproducible, version-controlled pipeline development practices. • Develop and optimize data warehouse models to support analytics use cases, ensuring performance, scalability, and usability. • Implement automated testing, monitoring, and data quality controls to identify and resolve issues proactively. • Improve storage efficiency and query performance to support scalability and effective cloud cost management (FinOps). • Work with data analysts and business stakeholders to understand requirements and ensure timely and reliable data availability for reporting, analytics, and machine learning use cases.