Senior Data Engineer architecting cloud data platforms and pipelines for Arc’teryx supply chain analytics. Leading migrations, governance, observability, and engineering mentorship.
About the role
- Staff Data Engineer building pipelines, models, and infrastructure for exacare ai’s post-acute care AI platform. Turning healthcare data into reliable foundations for product, analytics, and ML workflows.
Responsibilities
- 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
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