Note: The job is a remote job and is open to candidates in USA. exacare ai is a health tech company building an AI operating system for post-acute care. The Staff Data Engineer will build pipelines, data models, and infrastructure that support the company’s product, analytics, and AI workflows. The role owns complex data initiatives end-to-end, ensuring reliable, scalable, and usable healthcare data foundations.
Responsibilities
- Own data initiatives end-to-end: Take a data problem through technical design, implementation, validation, production rollout, and ongoing operation, without requiring constant senior oversight
- Build reliable data pipelines: Design and maintain scalable ingestion and transformation pipelines across application databases, APIs, third-party integrations, and healthcare data sources
- Bring systems-to-workflow thinking: Connect data architecture, models, and integrations to how customers, operators, and internal teams use the resulting information
- Design reusable data models: Create well-structured datasets and shared definitions that support product features, reporting, analytics, and machine learning
- Own data quality and production readiness: Build validation, monitoring, alerting, and recovery into pipelines so teams can trust the accuracy, freshness, and completeness of their data
- Scale performance and reliability: Improve processing efficiency, query performance, and infrastructure costs as data volume and product complexity grow
- Collaborate across teams: Partner with product, operations, platform, ML, and other engineers to translate business needs into practical data solutions
- Raise the engineering bar: Contribute to technical design, code reviews, documentation, and mentorship while establishing maintainable data engineering practices
Skills
- 7+ years of engineering experience with a focus on data engineering, data platforms, or backend systems involving substantial data processing
- Strong proficiency with SQL and TypeScript, including building production pipelines and working with complex datasets
- Experience designing ETL/ELT pipelines, data models, and orchestration workflows, with an understanding of dependencies, retries, backfills, and schema evolution
- Strong fundamentals in relational databases, data warehouses, and cloud infrastructure, including 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. You understand how data will be used and make technical decisions that serve those needs
- A practical approach to data quality, observability, access controls, and handling sensitive information
- Comfort operating in ambiguity. You can scope, plan, and execute independently on complex, open-ended problems
- Clear communication with technical and nontechnical partners, including the ability to explain data assumptions, tradeoffs, and limitations
- Experience working with healthcare data, EHR integrations, or interoperability standards such FHIR
- Experience supporting ML pipelines, AI products, or datasets used for model training and evaluation
- Familiarity with tools such as Databricks, dbt, Airflow, Dagster, Spark, or comparable data processing and orchestration frameworks
- Experience building data infrastructure in a fast-growing startup
Benefits
- Remote in Toronto and Vancouver; hybrid in New York
Company Overview
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