ExaCare AI
ExaCare AI
ExaCare AI izstrādā SaaS programmatūru pēcakūtās aprūpes pakalpojumu sniedzējiem, tostarp kvalificētās aprūpes iestādēm un mājas veselības aprūpes aģentūrām. Tās platforma atbalsta nosūtījumu pārskatīšanu, apdrošināšanas pārbaudi, atlīdzību procesu pārvaldību un pacientu uzņemšanu, apkopojot klīniskos ierakstus, identificējot klīniskos un finanšu riskus, piemēram, PDPM iespējas, pārbaudot apdrošināšanas seguma atbilstību un nodrošinot pārskatāmu informāciju par noslogojumu, pacientu skaita tendencēm un darbības rezultātiem.

Staff Data Engineer, Remote in Canada | ExaCare AI

Lead data engineering for ExaCare AI’s post-acute care platform, building pipelines, models, and infrastructure. Create reliable foundations for product, analytics, and machine learning workflows using healthcare data.

Apraksts

  • Lead data initiatives from technical design and implementation through validation, launch, and ongoing operations
  • Build and maintain scalable ingestion and transformation pipelines for application databases, APIs, third-party integrations, and healthcare data
  • Align data architecture, models, and integrations with customer, operator, and internal workflows
  • Develop reusable data models and shared definitions for product features, reporting, analytics, and machine learning
  • Add validation, monitoring, alerting, and recovery capabilities to data pipelines
  • Increase processing efficiency and query performance while managing infrastructure costs as data and product complexity grow
  • Work with product, operations, platform, ML, and engineering teams to turn business needs into data solutions
  • Contribute to technical design, code reviews, documentation, mentoring, and sustainable data engineering practices

Prasības

  • At least 7 years of engineering experience in data engineering, data platforms, or backend systems with substantial data processing
  • Strong SQL and TypeScript skills
  • Experience designing ETL/ELT pipelines, data models, and orchestration workflows
  • Understanding of dependencies, retries, backfills, and schema evolution
  • Strong knowledge of relational databases, data warehouses, and cloud infrastructure
  • Experience optimizing queries and building scalable storage and processing systems
  • Ability to own production data systems from design through launch and ongoing support
  • Sound judgment about products and workflows
  • Practical experience with data quality, observability, access controls, and sensitive information
  • Ability to independently scope, plan, and deliver complex, open-ended work
  • Clear communication with technical and nontechnical partners
  • Healthcare data, EHR integration, or interoperability experience, including standards such as FHIR, is a plus
  • Experience supporting ML pipelines, AI products, or datasets for model training and evaluation is a plus
  • Familiarity with Databricks, dbt, Airflow, Dagster, Spark, or similar frameworks is a plus
  • Experience building data infrastructure at a fast-growing startup is a plus

Priekšrocības

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

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