# Data Engineer at Tebra

> Remote · United States · Unspecified · Data engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Atebra%3A4710332005 (read 2026-11-05).

## Facts (observed)

- Company: [Tebra](https://alderhq.app/companies/tebra)
- Location text: United States - Remote; US - Remote
- Where it can be done from (inferred): Remote · United States (evidence: "Location: United States - Remote; US - Remote")
- Posted: 2026-07-27 (101 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/tebra/jobs/4710332005
- Skills listed (inferred): Python, Kafka, Spark, Airflow, Snowflake, SQL

## Pay and sponsorship

- Posted pay: $128k–$145k per year (from the listing's pay field)
- Market band for similar posted roles (United States, n 168): p25 $135k, median $156k, p75 $190k

## Description (the employer's text)

Tebra only initiates contact with candidates via email from an official Tebra email address (@tebra.com, @patientpop.com, or @kareo.com) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging.

### About the Role

As a Data Engineer focused on AI/ML, you’ll build, maintain, and optimize the data infrastructure that powers Tebra’s intelligent features. You’ll partner closely with Machine Learning Engineers, Data Scientists, and Software Engineers to transform complex healthcare data into high-quality datasets and real-time features that enable machine learning models.

This is a hands-on engineering role where you’ll contribute to scalable data pipelines, improve data quality, and help ensure our AI systems are powered by reliable, performant, and well-governed data. You’ll work on modern data platforms and gain experience building solutions that support both model training and production inference.

### Your Area of Focus

- Design, build, and maintain scalable data pipelines for feature extraction, training data generation, and model monitoring.
- Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies.
- Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness.
- Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation.
- Build reusable data engineering components, including automated data quality checks, schema validation, and testing frameworks.
- Translate business requirements into scalable data solutions that enable analytics and machine learning use cases.
- Optimize SQL queries, Spark workloads, and data processing pipelines to improve performance and scalability.
- Collaborate with ML Engineers and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility.
- Break down technical work into manageable tasks and deliver high-quality solutions within an agile team.
### Your Professional Qualifications

- 3+ years of professional experience in Data Engineering, Software Engineering, or a related field.
- 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads.
- Strong proficiency in Python and SQL with experience developing production-quality data pipelines.
- Experience with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms.
- Experience working with cloud-based data platforms such as Databricks, Snowflake, Delta Lake, or equivalent lakehouse technologies.
- Understanding of data modeling, data warehousing, and data governance best practices.
- Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts.
- Experience deploying and supporting production data pipelines with monitoring, testing, and CI/CD practices.
- Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams.
- Excellent communication skills and a desire to continuously learn new technologies and engineering practices.
### #LI-SS1 #LI-Remote

We are dedicated to attracting and retaining top talent with competitive and fair compensation. For this position, this range reflects our Zone 1 (National Average) pay band. Your specific compensation is thoughtfully determined by your experience, qualifications, the specific requirements of the role, and your Geo Zone. Our geo-zone system ensures your pay is competitive for your location, recognizing varying costs of labor across regions.

Our four geo zones are designed to reflect this:

Zone 1: National Average

Zone 2: Moderately Higher Cost Regions

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Atebra%3A4710332005 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.