Senior Data Engineer- Databricks

Risepoint is an education technology company that provides world-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high-ROI, workforce-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.

About the Role

We're looking for a hands-on Data Engineer to build reliable, scalable data pipelines on Databricks. You'll turn requirements into production-grade ELT/ETL jobs, Delta Lake tables, and reusable components that speed up our teams. In this role, you'll implement and improve reference patterns, optimize Spark for performance and cost, apply best practices with Unity Catalog and workflow orchestration, and ship high-quality code others can build on. If you love solving data problems at scale and empowering teammates through clean, well-documented solutions, you'll thrive here.

Key Responsibilities

• The ideal candidate will have a strong background in building scalable data pipelines, optimizing big data workflows, and integrating Databricks with cloud services
• This role will play a pivotal part in enabling the customer's data engineering and analytics initiatives—especially those tied to AI-driven solutions and projects—by implementing cloud-native architectures that fuel innovation and sustainability
• Partner directly with the customer's data engineering team to design and deliver scalable, cloud-based data solutions
• Execute complex ad-hoc queries using Databricks SQL to explore large lakehouse datasets and uncover actionable insights
• Leverage Databricks notebooks to develop robust data transformation workflows using PySpark and SQL
• Design, develop, and maintain scalable data pipelines using Apache Spark on Databricks
• Build ETL/ELT workflows with AWS and Azure Services
• Optimize Spark jobs for both performance and cost within the customer's cloud infrastructure
• Collaborate with data scientists, ML engineers, and business analysts to support AI and machine learning use cases, including data preparation, feature engineering, and model operationalization
• Contribute to the development of AI-powered solutions that improve operational efficiency, route optimization, and predictive maintenance in the waste management domain
• Implement CI/CD pipelines for Databricks jobs using GitHub Actions, Azure DevOps, or Jenkins
• Ensure data quality, lineage, and compliance through tools like Unity Catalog, Delta Lake, and AWS Lake Formation
• Troubleshoot and maintain production data pipelines
• Provide mentorship and share best practices with both internal and customer teams

Qualifications

• Bachelor's degree in computer science, Engineering, or a related field
• 5+ years of experience in software/data engineering, including at least 2 years working with Databricks and Apache Spark
• Strong proficiency in Python, SQL, and PySpark
• Deep understanding of AWS and Azure Cloud service
• Experience with Databricks Data LakeHouse, Databricks Workflows, and Databricks SQL, dbt
• Solid grasp of data Lakehouse and warehousing architecture
• Prior experience supporting AI/ML workflows, including training data pipelines and model deployment support
• Familiarity with infrastructure-as-code tools like Terraform or CloudFormation
• Strong analytical and troubleshooting skills in a fast-paced, agile environment
• Excellent collaboration skills for interfacing with both technical and non-technical customer stakeholders
• Clear communicator with strong documentation habits
• Comfortable leading discussions, offering strategic input, and mentoring others

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