About the role
ABOUT
THE ROLE: We are seeking a full-time Data Engineer III to join our team and define and deploy mission-critical data infrastructure. The primary responsibility for this role is defining the processing of large healthcare datasets, which enable Rightway to generate insights that improve the health of more than 3 million members. In this role, you will bring deep expertise in SQL and Python to build and maintain production-grade data pipelines within the AWS ecosystem — and you will leverage AI tooling to augment your workflow, accelerate development, and continuously improve the reliability and efficiency of our data platform. While familiarity with medical data is beneficial, it is not a requirement. WHAT YOU’LL DO: Write production-level Python and SQL code, with a strong emphasis on complex SQL for data transformation, pipeline logic, and analytical query optimization. Leverage AI tools and large language models to augment engineering workflows — from code generation and query optimization to automated testing, documentation, and process improvement. Utilize AWS services such as Lambda, Glue, and Fargate for scalable data operations. Build and maintain large-scale data pipelines using Apache Airflow. Oversee the maintenance of Extract, Load, and Transform (ELT) processes using DBT. Design optimized database schemas in PostgreSQL and Amazon Redshift to ensure data integrity and high performance. Identify opportunities to apply AI-driven automation to repetitive or high-volume data engineering tasks, reducing manual effort and improving throughput.
WHO YOU ARE: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent work experience. 5+ years of industry experience as a data engineer. Expert-level SQL skills, including query optimization, schema design, and working with large-scale datasets in production environments. Strong Python proficiency with experience writing and maintaining production-grade code. Skilled in working with AWS systems in a production environment. Experienced in scaling data infrastructure to handle high volumes of data. Demonstrated ability to incorporate AI tools (e.g. GitHub Copilot, LLM-assisted development) into an engineering workflow to improve speed and quality. EXTRA CREDIT: Experience working with medical and pharmacy claims data. Knowledge in deploying applications using serverless architecture. Hands-on experience managing AWS services such as Lambda, Glue, and Fargate. Experience with Master Data Management and data governance. Familiarity with infrastructure-as-code services such as Terraform or CloudFormation. Experience using AI tooling to automate data quality checks, generate pipeline scaffolding, or accelerate ETL development.
