Senior Data Engineer - Remote

Experity is a mission-driven company transforming on-demand healthcare across the U.S. through software that supports urgent care clinics. The Senior Data Engineer will design, build, and operate scalable data pipelines and platform capabilities supporting analytics, AI/ML, and healthcare products while providing technical leadership and mentoring engineers.

Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines for batch and near-real-time data processing
• Integrate data from databases, APIs, applications, event streams, and external sources into Experity's enterprise data platform
• Build reusable, high-quality data models and curated data products for analytics, reporting, AI/ML, and operational applications
• Develop complex data transformations and processing workflows using Python, SQL, Snowflake, and distributed data technologies
• Design data solutions for scalability, availability, resiliency, performance, and cost efficiency
• Develop reusable frameworks, libraries, and engineering patterns that improve developer productivity and data pipeline consistency
• Implement automated testing, CI/CD, Infrastructure as Code, and DataOps practices across data engineering workflows
• Build monitoring, alerting, data observability, and automated remediation capabilities to ensure pipeline reliability and data integrity
• Troubleshoot complex production issues and drive root-cause analysis and long-term improvements
• Continuously optimize data pipelines, queries, storage, and compute for performance and cost
• Implement data quality, validation, lineage, and reconciliation controls across critical data pipelines
• Partner with Data Architecture, Governance, and Security teams to ensure data solutions follow enterprise standards
• Ensure healthcare data is handled securely and in accordance with applicable privacy, security, and compliance requirements
• Provide technical leadership through solution design, architecture discussions, code reviews, and engineering best practices
• Mentor Data Engineers and help improve engineering quality and technical capabilities across the team
• Partner with Data Scientists and Machine Learning Engineers to build reliable datasets, feature pipelines, and data infrastructure for AI/ML applications
• Collaborate with Product, Engineering, Analytics, and business stakeholders to translate requirements into scalable data solutions
• Maintain clear technical documentation for data pipelines, models, architecture, and operational processes

Requirements:

• • Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field
• • 5+ years of experience building production-grade data pipelines, data platforms, and large-scale data processing solutions
• • Advanced proficiency in Python and SQL with strong software engineering fundamentals
• • Hands-on experience with Snowflake and cloud-based data platforms
• • Experience with Kafka, Flink, or other distributed and streaming technologies
• • Strong experience with AWS, including services such as S3, Lambda, Glue, Kinesis, or equivalent cloud technologies
• • Experience designing and optimizing ETL/ELT pipelines, data models, and high-volume data processing workflows
• • Experience with orchestration technologies such as Airflow or equivalent platforms
• • Experience with CI/CD, Git, automated testing, and Infrastructure as Code
• • Strong understanding of data quality, governance, security, observability, and production support
• • Strong problem-solving, communication, and cross-functional collaboration skills
• • A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end
• • Experience transforming data leveraging dbt, preferably dbt cloud
• • Experience working in AI-native engineering environments and effectively leveraging AI-assisted development tools to improve engineering velocity, code quality, and operational efficiency
• • Experience supporting Machine Learning and Generative AI workloads, including feature engineering and ML data pipelines
• • Experience with Docker, Kubernetes, Terraform, or CloudFormation
• • Experience with data cataloging, lineage, metadata management, and data observability platforms
• • Experience in Healthcare, HealthTech, SaaS, or other regulated industries