Senior Backend Data Engineer at Eleos Health

1.54M jobs liveBrowse

Senior Backend Data Engineer at Eleos Health · Katori

Asks for

The ad says“Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka”
The ad says“Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka”
The ad says“Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS”
The ad says“Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS”
The ad says“Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka”
The ad says“API Development: FastAPI, micro-service architecture, SFTP”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)”
The ad says“ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)”
The ad says“Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“Familiarity with modern LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, and experience supporting their deployment or integration.”
The ad says“API Development: FastAPI, micro-service architecture, SFTP”
The ad says“ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)”
The ad says“At least 5 years of experience with Python in backend or data engineering roles, designing and operating large-scale data pipelines, backend services, and data infrastructure in production environments.”
The ad says“Familiarity with modern LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, and experience supporting their deployment or integration.”
The ad says“Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS”
The ad says“Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)”
The ad says“API Development: FastAPI, micro-service architecture, SFTP”
The ad says“Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS”

Who is Eleos Health?

The people who deliver care in our communities and homes do some of the most important work in healthcare. But too often, theyʼre asked to spend their time navigating paperwork, administrative burden, and problems they only discover after something has already gone wrong.

Eleos exists to change that.

Weʼre building the system of action for community-based care - helping behavioral health, hospice, home health, and other care teams spend less time reacting to problems and more time focused on the people they serve.

Powered by our Polaris AI, Eleos works alongside clinicians and their teams to understand whatʼs happening across care in real time and take action before issues become problems, reducing administrative burden, protecting revenue and compliance, and giving care teams more time and capacity to deliver great care.

Our vision is a world where care teams can stay one step ahead, rather than constantly catching up. Weʼre helping shift an entire industry from reactive to proactive, and we need talented people to help us get there.

What is this opportunity?
We are seeking a Senior Backend Engineer – Data Platform to join our expanding team and play a crucial role in designing, building, and maintaining robust and scalable data pipelines and infrastructure. In this role, you will directly enable data-driven decision-making and support the development and deployment of AI/ML products that power Eleos Health.

You’ll collaborate closely with engineering, product, and data science teams to ensure our data systems are high-quality, resilient, and scalable as we grow. As a Senior Backend Engineer on our Data Platform team, you will drive efforts to deliver reliable, efficient, and consistent data services across the organization. You will also help enable the rapid development and deployment of advanced features, insights, and AI-driven capabilities that improve outcomes for clinicians and clients.

Who are you?
You are a seasoned backend or data engineer with experience working on production-grade ML/AI-powered products. You thrive in fast-paced, high-ownership environments and are passionate about building scalable and reliable systems. You understand the unique requirements of delivering AI/ML features in production, and you are comfortable working with modern technologies in the LLM/RAG ecosystem.
You pride yourself on delivering high-quality solutions quickly, without sacrificing design or reliability. You’re known for your responsiveness, collaborative spirit, and service-oriented mindset—especially when you’re on-call and the stakes are high.How will you contribute?

Design, implement, and maintain scalable and reliable data pipelines and backend systems supporting both operational and analytical needs, with a focus on ML/AI product enablement.
Ensure data processing is optimized for speed, efficiency, and fault tolerance, enabling seamless integration with AI/ML workflows and reliable performance across all Eleos Health products.
Monitor and improve uptime, reliability, and observability of our data infrastructure and pipelines.
Build and maintain systems to ensure data quality, consistency, and usability across the organization, enabling advanced analytics and AI solutions.
Work closely with product and engineering teams to deliver new features rapidly and with a high standard of technical excellence.
Drive innovation in how we build, measure, and optimize data features, backend services, and AI product integrations.
Participate in on-call rotations with a service-oriented approach and fast responsiveness.
Lead scalability efforts to support increasing data volumes, expanding AI/ML initiatives, and new product launches.

What qualifications and skills will help you to be successful?

At least 5 years of experience with Python in backend or data engineering roles, designing and operating large-scale data pipelines, backend services, and data infrastructure in production environments.
Hands-on experience working on ML/AI-powered products in production, with strong understanding of requirements for integrating data platforms with AI features.
Familiarity with modern LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, and experience supporting their deployment or integration.
Familiar with or have worked with these technologies (or alternatives):
- Data Processing & Streaming: Apache Spark, DBT, Airflow, Airbyte, Kafka
- API Development: FastAPI, micro-service architecture, SFTP
- Data Storage: Data Lakehouse architectures, Apache Iceberg, Vector Databases, RDS
- ML/AI: ML/LLM libraries and frameworks (such as Gemini, Hugging Face, etc.)
- Cloud Infrastructure: AWS stack (S3, Firehose, Lambda, Athena, etc.), Kubernetes (K8s)
Demonstrated ability to optimize performance and ensure high availability, scalability, and reliability of backend/data systems.
Strong foundation in best practices for data quality, governance, security, and observability.
Ability to collaborate effectively with engineering, data science, and product teams in a cross-functional setting.
Track record of innovative thinking on feature-level implementations, metric definitions, and AI/data integrations.
Service-oriented approach, particularly in high-responsibility, on-call situations.

This is a unique opportunity to join a startup that is having a meaningful impact on the well-being and mental health of thousands. We have

A product that positively impacts peoples' lives every single day.
A team of amazing people with a shared vision and the infinite drive to make it happen
We offer significant equity.
Opportunity to build, grow and become highly instrumental in shaping how technology can increase the effectiveness of therapy.
Hybrid work opportunities.
Mental health days off you can take any given moment simply because you need them.