Staff Data Engineer

Job Description:

• Own data initiatives end-to-end, from technical design through implementation, validation, production rollout, and ongoing operation
• Design and maintain scalable ingestion and transformation pipelines across application databases, APIs, third-party integrations, and healthcare data sources
• Connect data architecture, models, and integrations to customer, operator, and internal team workflows
• Create reusable data models and shared definitions supporting product features, reporting, analytics, and machine learning
• Build validation, monitoring, alerting, and recovery into pipelines
• Improve processing efficiency, query performance, and infrastructure costs as data volume and product complexity grow
• Partner with product, operations, platform, ML, and engineering teams to translate business needs into data solutions
• Contribute to technical design, code reviews, documentation, mentorship, and maintainable data engineering practices

Requirements:

• 7+ years of engineering experience focused on data engineering, data platforms, or backend systems involving substantial data processing
• Strong proficiency with SQL and TypeScript
• Experience designing ETL/ELT pipelines, data models, and orchestration workflows
• Understanding of dependencies, retries, backfills, and schema evolution
• Strong fundamentals in relational databases, data warehouses, and cloud infrastructure
• Experience with query optimization and scalable storage and processing
• Ability to own production data systems end-to-end, from design through rollout and ongoing support
• Good product and workflow judgment
• Practical approach to data quality, observability, access controls, and handling sensitive information
• Ability to scope, plan, and execute independently on complex, open-ended problems
• Clear communication with technical and nontechnical partners
• Experience with healthcare data, EHR integrations, or interoperability standards such as FHIR is nice to have
• Experience supporting ML pipelines, AI products, or datasets used for model training and evaluation is nice to have
• Familiarity with Databricks, dbt, Airflow, Dagster, Spark, or comparable frameworks is nice to have
• Experience building data infrastructure in a fast-growing startup is nice to have

Benefits:

• Remote work option in Toronto and Vancouver
• Hybrid work option in New York