Senior Data Engineer

Skills

About the Role

You will own and evolve the data platform, including the BigQuery warehouse, dbt transformation layers, Airflow/Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric. You will build and operate the ML platform, from training pipelines on Kubeflow and Vertex AI to model serving with FastAPI behind Vertex endpoints, CI/CD, containerization and typed contracts. You will harden and standardize the data models the business depends on, improve schemas, fix data-quality issues and establish trustworthy source-of-truth feeds. You will establish data governance and observability, bringing data outside the warehouse under proper governance and building operational metrics for products that don't yet have them. You will standardize data engineering patterns, tooling and pipelines across product lines and partner with data science, backend and product teams on the producer-to-consumer contract. You will build scalable data pipelines processing large volumes of financial data, design reliable datasets, data models and feature pipelines, and develop cost-efficient, high-performance data services supporting real-time and batch workloads.

Requirements

• 5+ years of hands-on Data Engineering experience designing, building and operating production data platforms, pipelines and warehouse solutions in a cloud environment
• Strong experience with ETL/ELT development, data modeling, schema design, orchestration, data quality, lineage and warehouse optimization
• Experience with BigQuery, dbt, Airflow or equivalent modern data tooling highly desirable
• Expert SQL and strong Python skills
• Experience with cloud-native data ecosystems including data warehouses, event-driven architectures, distributed processing and platform observability
• Demonstrated ownership of production systems including monitoring, reliability, performance tuning, cost optimization and incident response
• Experience supporting machine learning workflows, feature pipelines, model-serving infrastructure or MLOps environments is an asset
• Ability to partner effectively with Data Science, Product, Engineering and QA teams
• Bachelor's degree in Computer Science, Data Engineering, Software Engineering or a related technical field, or equivalent practical experience
• Must be legally authorized to work in Canada

Responsibilities

• Own and evolve the data platform, including BigQuery warehouse, dbt transformation layers, Airflow/Cloud Composer orchestration and Pub/Sub ingestion
• Build and operate the ML platform, including Kubeflow training pipelines on Vertex AI, FastAPI model serving, CI/CD, containerization and typed contracts
• Harden and standardize business-critical data models by improving schemas, fixing data-quality issues and establishing source-of-truth feeds
• Establish data governance and observability, bringing external data under governance and building operational metrics
• Standardize data engineering patterns, tooling and pipelines across product lines
• Partner with data science, backend and product teams on the producer-to-consumer data contract
• Build scalable data pipelines that process and transform large volumes of financial data
• Design and maintain reliable datasets, data models and feature pipelines for ML and product teams
• Develop cost-efficient, high-performance data services supporting real-time and batch workloads

Benefits

• Health and dental coverage as of day 1
• Flexible paid time off
• Remote work environment with frequent in-person gatherings and activities