> Markdown version of [/jobs/ext/4555962-databricks-developer](https://www.wearedevelopers.com/jobs/ext/4555962-databricks-developer). 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). --- # Databricks Developer - **Company:** Everforth Apex - **Location:** Houston, TX, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Python (Programming Language), SQL Databases, Information Technology, Legacy Systems, Databricks - **Published:** November 4, 2026 - **Apply:** https://www.dice.com/job-detail/87f56208-680e-41ee-b408-d0c3a966999a ## About the Role Education: A bachelor's degree in Computer Science, Information Technology, or a related field is preferred, though equivalent professional experience will be considered. Experience: Mid-level experience in data engineering with a focus on supporting analytics and reporting use cases. Technical Skills: Strong hands-on experience with Databricks, proficiency in Python and SQL, understanding of data modeling concepts, and familiarity with Bronze/Silver/Gold lakehouse architectures and Azure DevOps deployment processes. Preferred Qualifications * Experience with Unity Catalog. * Familiarity with AWS cloud environments. * Experience leveraging Databricks Genie or other AI-assisted development tools. * Background in supporting customer loyalty or large-scale analytics data initiatives. ## Description * Develop and optimize analytics-ready data products within the Databricks platform. * Build and maintain Silver-layer data transformations, tables, and materialized views. * Collaborate with business stakeholders and end users to gather requirements and define data needs. * Support the broader team of data engineers in migrating data assets from legacy systems into Databricks. * Implement data modeling best practices to ensure scalable and reliable analytics workloads. * Utilize CI/CD deployment processes to promote code changes efficiently.