> Markdown version of [/jobs/ext/3841073-data-engineer](https://www.wearedevelopers.com/jobs/ext/3841073-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer

- **Company:** Everforth Apex
- **Location:** Cleveland, OH, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Cloud Database, Databases, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Software Tools, SQL Databases, Google Cloud, Data Delivery, Data Pipelines
- **Published:** October 20, 2026
- **Apply:** https://www.dice.com/job-detail/5a31ba14-4c40-4b5a-8543-cf754151fa09

## About the Role

Education: A master's degree is required.

Experience: 1-3 years of professional experience in data engineering.

Technical Skills: Experience with cloud-based data engineering (e.g., Azure, AWS, Google Cloud Platform). Proficiency in building data pipelines, Python, SQL, and ETL processes.

Preferred Qualifications

 * Experience with MS Fabric is a preference.
 * Previous experience in healthcare, health systems, health insurance, or academia.
 * Familiarity with healthcare claims data.
 * Experience with Epic systems.
 * Microsoft certifications are considered a plus.

## Description

We are seeking a Data Engineer to join an analytics team. This role is focused on building and maintaining data pipelines and infrastructure in a cloud-based environment. The ideal candidate will work closely with data scientists and other team members to ensure the reliability and efficiency of data workflows, directly contributing to the team's analytical capabilities., * Develop, construct, test, and maintain data architectures, such as databases and large-scale processing systems.

 * Build robust and scalable data pipelines using Python, SQL, and ETL processes.
 * Work with modern cloud-based data engineering tools, with a focus on cloud platforms such as Azure, AWS, or Google Cloud Platform.
 * Collaborate with data scientists and analytics team members to understand data needs and resolve issues.
 * Communicate technical challenges and solutions effectively within the team.
 * Ensure data delivery is timely and meets quality standards.