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ExaCare

Staff Data Engineer

Posted 2 Days Ago
In-Office or Remote
Hiring Remotely in New York, IN
Senior level
In-Office or Remote
Hiring Remotely in New York, IN
Senior level
Own end-to-end data initiatives, including scalable ingestion and transformation pipelines, reusable data models, orchestration, validation, monitoring, and production operations. Improve performance, reliability, and infrastructure costs while partnering with product, operations, platform, ML, and engineering teams. Establish data engineering standards through technical design, code reviews, documentation, mentorship, and strong data quality and security practices.
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About exacare ai


exacare ai is a leading health tech company on a mission to build the AI operating system for post-acute care. Our platform turns messy, unstructured referral packets into clear clinical insights and next steps, so teams can make faster, safer placement decisions with less administrative burden. Today, exacare ai powers more than 2,500 facilities, and is growing rapidly.


We recently raised a $30M Series A led by Insight Partners, and are bringing world-class talent together to transform healthcare. If you like building, learning, and want to make a real impact, come join us!

We’re looking for a Staff Data Engineer who thrives in an environment with strong ownership and can drive complex data initiatives from concept to production. You’ll build the pipelines, data models, and infrastructure that power our product, analytics, and AI workflows; turning complex healthcare data into reliable, usable foundations for our teams and customers.

What You’ll Do
  • 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.
What You’ll Bring
  • 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.
Nice to Have
  • 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.



An insight into our Core Values


Only the Best Belong Here 

We are unapologetic about talent. This should be the best team you have ever been on. Protecting that standard is how we honor each other’s time, ambition, and craft.


We work even harder to keep our partners than we did to earn them initially

The work does not stop when a customer first onboards to our platform. It deepens over time. We partner with operators, listening and learning about real problems, and translate that into solutions that help them succeed in practice. We earn trust through consistent delivery. 


The Patient is Downstream of Every Decision
At the end of the day, this is about the patient. We get there by deeply respecting post-acute operators and partners, because their work is the path our software travels to create better care.


Raise the Bar on Ownership

We grow because people here go beyond the minimum. We invest extra effort, care, and ownership into what we build.


The World is Moving Fast. We move Faster.

This is a race. We work hard, we move early, and we stay ahead of problems and competitors. If we slow down, someone else will pass us.


Radical Candor, Zero Politics.

We say what’s true, early, and we keep communication direct and clean so the team can move.


Bring Good Vibes and Win Together 

We win as a team. We bring energy, support each other, and make the workplace somewhere people are excited to show up to.


If this sounds like you, we'd love to have a chat!


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