Role Description As a Senior Data Platform Engineer, you will be instrumental in driving the strategic evolution and design of our data platform investments and data pipelines, working in close partnership with Architects. You will provide technical leadership in identifying, monitoring, and driving initiatives that ensure our data platform remains scalable, reliable, and efficient amidst evolving product demands. - Lead scoping and execution of critical improvements to our data platform to maintain overall system health and improve data observability in lieu of changing product needs, and to optimize innovation velocity. - Support production ML Ops functionality and advance the quality of our core ML & LLM platform capabilities. - Partner strategically with data science, analytics, and data engineering leads and Architects to gather feedback and drive the development of scalable platform solutions that unlock new features within the defined architectural framework. - Provide expertise on the overall data engineering best practices, standards, architectural approaches and complex technical resolutions. - Support solution development; translate product/analytical vision into highly functional data pipelines supporting high quality & highly trusted data products (incl. designing data structures, building and scheduling data transformation pipelines, improving transparency etc.). Qualifications - Strong cross-functional communication - ability to break down complex technical components for technical and non-technical partners alike. - 4+ years of hands-on experience designing, building, and operating cloud-based, highly available, observable, and scalable data platforms utilizing large, diverse data sets in production to meet ambiguous business needs. - Excellence in quality data pipeline design, development, and optimization to create reliable, modular, secure data foundations for the organization's data delivery system from applications to analytics & ML. - Experience building, designing, and/or developing on a diverse set of modern data architecture designs and their relative capabilities and use cases (ex. Data Lake, Lakehouse). - Experience working with Databricks and deployed production grade pipelines. - Python and DBT SQL. Requirements - Proficiency in interpreting complex datasets, including the ability to discern underlying patterns, identify anomalies, and extract meaningful insights, demonstrating advanced data intuition and analytical skills with the ability to translate these insights into recommendations for platform improvements that align with the overall architecture. - Relevant industry certifications in various Data Architecture services (SnowPro Advanced Architect, Azure Solutions Architect Expert, AWS Solutions Architect / Database, Databricks Data Engineer / Spark / Platform etc.). - Experience designing and supporting multi-cloud architectures (particularly for ML / AI systems). - Experience with data visualization tools and analytics technologies (Sigma, Looker, PowerBI, etc.). - Degree in Computer Science, Engineering, or related field. - Experience working with healthcare data and HIPAA data protection. Benefits - Open Paid Time Off. - Paid parental leave. - Professional development. - Wellness and technology stipends. - A generous employee referral bonus. - Employee stock option awards.

Role Description We are seeking a SAS Viya Engineer/Developer to design, build, and manage data pipelines, analytics workflows, and data engineering processes using SAS Viya. This role focuses on transforming and governing data, implementing DataOps practices, and optimizing legacy workloads while enabling high-performance analytics through distributed processing. Key Responsibilities: - Design and manage data pipelines and analytics workflows using SAS Viya - Build, schedule, and execute complex ETL/ELT processes for large-scale data - Prepare, transform, and govern data across the SAS data lifecycle - Implement DataOps practices including automated data scanning, profiling, and quality checks - Utilize CAS distributed computing for parallel data processing and performance optimization - Manage data access, lineage, and cataloging to support data governance - Integrate SAS Viya with external sources (SQL databases, Spark, and big data platforms) - Migrate and optimize legacy SAS jobs into the SAS Viya lifecycle - Collaborate with developers and stakeholders to improve data workflows and performance Qualifications - 5+ years of experience in data engineering, analytics engineering, or SAS platform development - Hands-on experience working within SAS Viya environments - Experience supporting enterprise or mission-critical data platforms - Strong experience with SAS Viya and CAS distributed computing architecture - Experience designing ETL/ELT pipelines for big data environments - Understanding of DataOps principles and automation for data quality and governance - Experience with data lineage, access controls, and data cataloging - Integration experience with SQL databases and Apache Spark - Experience optimizing and migrating legacy SAS workloads - Must be US Citizen with ability to obtain Public Trust Clearance Requirements - Preferred Certification: SAS Certified Specialist: Administration of SAS Viya Benefits - Competitive compensation with opportunities for bonuses - Employer-paid health care - Training and development funds - 401k match

• Design and build modern data-centric software applications to support clinical and operational processes across all parts of the healthcare system • Leverage cloud computing, big data, data science, and modern software development methodologies and frameworks • Build data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces • Mentor and assist less experienced Data Engineers • Participate in research and development of new technologies

• Lead and manage daily and long-term operations of the Data Engineering team. • Oversee enterprise data pipelines, ETL/ELT processes, analytical platforms, and governance workflows. • Ensure adherence to enterprise data architecture, integration standards, governance practices, and security requirements. • Prioritize work across competing institutional demands while balancing service reliability and operational risk. • Guide the development of scalable data models, semantic layers, and metadata documents. • Coordinate incident response, data quality remediation, and continuous improvement initiatives. • Mentor staff in professional development, engineering best practices, and service delivery excellence. • Collaborate across IT and institutional leadership to support enterprise data initiatives.