Senior Data Engineer

About the role

QGenda is redefining healthcare workforce management everywhere care is delivered. We're on a mission to empower the healthcare industry to better onboarding, deploy, and manage their workforce. Over 4,500 healthcare organizations have trusted us to help them make strategic workforce decisions through our unified software platform. With more than 800 employees across the US, we are united in our vision and culture to make a difference for our customers, while enjoying the day-to-day. At QGenda, we value our employees and their contributions toward the success of the business. We strive to create a dynamic work environment that fosters growth, innovation, and collaboration, where employees can be proud of the work they do and the impact it has on the healthcare industry. QGenda is headquartered in Atlanta. To learn more about QGenda, visit us at qgenda.com or follow us on Instagram or LinkedIn . About Your Role As a Senior Data Engineer, you will design, build, and optimize the data platform, including pipelines, models, and infrastructure that power analytics, reporting, and data-driven decision making across the QGenda product lines. You will serve as a technical leader with the team, contributing to architectural direction, driving best practices, and supporting complex data initiatives. This role requires deep technical expertise, strong cross-functional collaboration, and the ability to deliver scalable, high-performing data systems that meet evolving business needs. How You'll Make an Impact Deliver High-Quality, Scalable Data Engineering Solutions Architect, develop, test, and maintain ELT/ETL pipelines and data workflows supporting high-volume analytics Implement advanced data processing solutions and observability techniques to ensure data is accurate, fresh, and reliable Design and refine data models and semantic layers that support analytical self-service and advanced reporting. Build data visualizations and dashboards supporting analytics use cases Strengthen Data Engineering Practices and Technical Standards Translate complex business and analytics requirements into efficient, scalable data solutions Apply best practices for version control, documentation, CI/CD, Infrastructure as Code, and data governance Participate in code reviews, identify opportunities for architectural improvement,, and contribute to continuous improvement efforts Collaborate Across Teams Partner with data engineers, DBAs, managers, and business stakeholders to deliver high-impact data products Provide technical guidance, informal mentorship, and support to other engineers in order to elevate team capabilities Communicate technical decisions, risks, and recommendations to both technical and non-technical audiences Drive Technical Excellence Optimize data pipelines and warehouse performance for speed, cost, and scalability Evaluate, prototype, and influence adoption of new tools, frameworks, and architectural patterns that enhance the data platform Contribute to data observability, incident response, and root-cause analysis for complex data issues Design and deliver AI-ready data products, ensuring data structures, metadata, and pipelines are suitable for natural language processing, predictive analytics, and other AI-driven capabilities

Requirements

• Exceptional analytical, problem solving, and debugging skills
• Strong communication with the ability to simplify and articulate technical concepts
• Ability to work collaboratively, influence architecture, and take ownership of deliverables
• Commitment to quality, reliability, and continuous improvement
• Experience You Bring
• 5-7+ years in data engineering/analytics engineering, or related field
• Bachelor's degree specializing in computing, data engineering, or related discipline
• Expertise in distributed data processing, data modeling, and performance tuning
• Strong proficiency in SQL and Python
• Experience with modern data stack components, such as:
• Cloud: AWS, GCP, Azure
• Warehouses: Snowflake, Redshift, BigQuery, etc.
• Orchestration: Airflow, MWAA, Composer, etc.
• Transformation: dbt, etc.
• Observability: data lineage/monitoring tools
• BI: Looker, Tableau, Power BI, etc.
• DevOps: Git, CI/CD, Terraform/CloudFormation
• Not Required, But Nice to Have
• Experience preparing datasets and data structures for AI/ML use cases, including NLP-driven analytics
• Experience with Glue, Dataflow
• #LI-Hybrid
• Applicants for this position must be authorized to work for any employer in the United States (U.S.), including being located in the US. We are unable to sponsor, take over sponsorship of, or hire candidates with an employment visa at this time.

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