• Design, develop, and implement robust ETL/ELT data pipelines for large-scale data ingestion, transformation, and storage. • Ensure data quality, integrity, and governance by implementing validation techniques, data monitoring, and automated testing. • Collaborate with cross-functional teams, including data scientists, analysts, platform engineers, and business stakeholders, to develop scalable and reusable data solutions. • Automate deployments and testing using CI/CD pipelines with Git, Terraform, GitHub Actions, or Jenkins. • Design and build custom data tools and abstractions to support analytics, machine learning, and real-time data processing. • Work with DevOps and platform teams to establish efficient deployment and monitoring processes for internal and external data products. • Develop and implement alerting, monitoring, and observability frameworks for data pipelines to ensure reliability and proactive issue resolution. • Contribute to the data architecture and strategy, driving improvements in scalability, performance, and cost optimization. • Stay up to date with emerging technologies and industry best practices to continuously enhance data engineering capabilities.

1upHealth is powering the digital transformation of the healthcare industry with our industry leading platform. By making healthcare data easily accessible and computable, we are on a mission to improve the patient experience, improve the quality of care, and reduce the overall cost of care. We are seeking an experienced Senior Data Engineer to join our team and play a crucial role in designing, implementing, and optimizing scalable data solutions. In this role, you will work with modern data technologies, distributed systems, and cloud platforms to build efficient, high-performance data pipelines that support analytics, AI/ML, and business intelligence initiatives. You will: - Design, develop, and implement robust ETL/ELT data pipelines for large-scale data ingestion, transformation, and storage. - Ensure data quality, integrity, and governance by implementing validation techniques, data monitoring, and automated testing. - Collaborate with cross-functional teams, including data scientists, analysts, platform engineers, and business stakeholders, to develop scalable and reusable data solutions. - Automate deployments and testing using CI/CD pipelines with Git, Terraform, GitHub Actions, or Jenkins. - Design and build custom data tools and abstractions to support analytics, machine learning, and real-time data processing. - Work with DevOps and platform teams to establish efficient deployment and monitoring processes for internal and external data products. - Develop and implement alerting, monitoring, and observability frameworks for data pipelines to ensure reliability and proactive issue resolution. - Contribute to the data architecture and strategy, driving improvements in scalability, performance, and cost optimization. - Stay up to date with emerging technologies and industry best practices to continuously enhance data engineering capabilities. We are looking for people who have: - 5+ years of hands-on experience in data engineering, with expertise in distributed data processing and big data frameworks (e.g., Apache Spark, Apache Iceberg, Trino, Apache Airflow, dbt, Dagster). - Advanced programming skills in Scala or Python for data transformation and automation. - Experience with real-time data streaming technologies such as Apache Flink, Spark Streaming, or Kafka. - Strong experience in performance tuning for Spark and optimizing large-scale data workflows. - Proficiency in SQL and database management, with hands-on experience in Massively Parallel Processing (MPP) databases such as Amazon Redshift, Snowflake, Teradata. - Familiarity with cloud-based data services (AWS, RDS, DynamoDB) and containerized infrastructure (EKS, Docker, Kubernetes). - Hands-on experience integrating DevOps and CI/CD practices in data engineering using GitHub Actions, Jenkins, or Terraform. - Proven ability to build monitoring, alerting, and observability tools for data pipelines to ensure high availability and reliability. - Experience in data mapping, validation, and testing frameworks to ensure accuracy and consistency. - Exposure in machine learning/deep learning using PyTorch, Tensorflow, or Keras (PyTorch preferred) - Exposure to machine learning workflows and familiarity with MLOps tools for model deployment and lifecycle management. - Self-starter with strong problem-solving skills who thrives in a fast-paced, agile environment. Nice to Have: - Experience working with healthcare data standards (e.g., HL7, FHIR) and regulatory compliance. - Familiarity with reporting and visualization tools such as Superset, Grafana, Tableau. - Understanding of data security, compliance, and governance frameworks (e.g., HIPAA, GDPR, SOC 2). Why Join Us? ✅ Impact-Driven Work – Be a key player in building data solutions that advance analytics and enable AI-driven innovations in the healthcare sector. ✅ Cutting-Edge Technology Stack – Work with modern big data, cloud, and AI-driven solutions. ✅ Collaborate with Experts – Join a team of top engineers to solve complex data challenges. ✅ Career Growth – Opportunity to lead projects and drive technical innovations. If you're passionate about building scalable data solutions and working with the latest in data engineering and AI-driven analytics, we’d love to hear from you! 🚀 Don't meet every requirement? Studies have shown that women, communities of color and historically underrepresented talent are less likely to apply to jobs unless they meet every single qualification. At 1upHealth we are dedicated to building a diverse, inclusive and authentic workplace. We encourage folks to apply if they meet at least 50% of the qualifications above. About 1upHealth At 1upHealth, our mission is to unlock health data and improve industry outcomes. As leaders in FHIR® interoperability, our platform makes it easier for partners to access, integrate, aggregate, and share data across a variety of systems. 1upHealth is building a data ecosystem to promote the digital transformation of the industry and encourage insight-driven healthcare. Benefits 100% Paid BCBS Medical and Dental Insurance for Employees Vision Insurance Flexible PTO Equity for All Employees 401(k) Home Office Stipend Lifestyle Savings Account Parental Leave (6 weeks bonding leave for all new parents + 10 weeks for birthing parents) Security Alert: 1upHealth only uses email domains of First Name. Last Name@1up.health or no-reply@1up.health to communicate with prospects. You will never receive an email from a third-party email service such as gmail. In addition, we will never ask a candidate for employment to share personal information (such as banking information, social security numbers, passport, etc), purchase their own equipment, or pay to apply to an open position.

• Collaborate with analytics and business teams to improve data models that feed business intelligence tools, increase data accessibility, and foster data-driven decision making across the organization. • Build and implement scalable solutions that align to our data governance standards and architectural roadmap for data integrations, data storage, reporting, and analytic solutions. • Design, develop, and test data integration solutions. Write, automate and document unit/integration/functional tests. • Manage automated deployments using Git pipelines, ensuring code changes are efficiently version-controlled and deployed. • Collaborate with multiple teams to streamline deployment workflows, monitor and troubleshoot pipelines for reliability and performance. • Perform data analysis required to troubleshoot data-related issues by building a data quality framework and assist in the resolution of data issues. • Serve as tech lead by mentoring less experienced members of the team through code reviews, pair programming and similar hands-on interactions.

• Engineer the Data Lifecycle: design and implement the "Golden Path" for data • Implement Data as Code: treat AWS-hosted MS SQL as a version-controlled automated ecosystem • Architect Multi-Layer Reliability: guarantee data quality and availability across all tiers • Optimize for Scalability & Performance: identify and resolve architectural bottlenecks • Standardize Data Observability: develop monitoring and alerting strategies • Bridge the Engineering Gap: collaborate with Software Engineers and Data Scientists