This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to design, build, and operate large-scale data services that power critical healthcare applications and APIs. The Senior Software Engineer will work on high-volume, complex data pipelines, collaborating across engineering and product teams to deliver reliable, scalable, and cost-efficient solutions. You will tackle operational challenges, optimize data processing workflows, and contribute to platform evolution while ensuring data privacy and regulatory compliance. This position involves working with state-of-the-art technologies, including cloud data warehouses, streaming pipelines, and modern data frameworks, in a fast-paced startup environment with mission-critical impact. Ideal candidates are innovative, collaborative, and motivated by open-ended technical challenges that directly improve healthcare outcomes. - Build, operate, and maintain scalable data services that drive applications, APIs, and data products. - Collaborate with engineering and product teams to prototype, develop, and deploy new functionality. - Ensure high data quality, accuracy, and freshness across large-scale datasets. - Implement and optimize streaming pipelines and batch processing workflows using modern data technologies. - Contribute to platform reliability, performance, and operational stability at scale. - Stay current with emerging tools and technologies to improve efficiency and innovation in data services. - Participate in cross-functional initiatives to enhance the platform’s functionality and compliance with regulatory requirements. Qualifications - Proven experience designing, developing, and operating operationally stable, scalable, and cost-efficient data services. - Experience with cloud data warehouses such as Snowflake, BigQuery, Redshift, or Databricks. - Strong programming skills in Go, Python, or similar languages. - Familiarity with data processing technologies such as Apache Spark, Apache Iceberg, dbt, or equivalent. - Knowledge of deployment technologies (GitHub Actions, CircleCI), cloud platforms (AWS, Azure, GCP), and Infrastructure as Code (Terraform, CloudFormation). - Experience working with large-scale datasets and distributed systems. - Strong problem-solving, collaboration, and communication skills, with the ability to thrive in a startup environment. - Experience with healthcare data and APIs, familiarity with FHIR and/or TEFCA standards is a plus. Benefits - Competitive compensation including base salary and equity. - Comprehensive health insurance and wellness benefits. - 401(k) plan with company match. - Unlimited Paid Time Off and flexible work arrangements. - Opportunity to work with a passionate team driving innovation in healthcare technology. - Exposure to cutting-edge data technologies and mission-critical projects. - Collaborative and supportive startup culture that encourages learning and growth.

Lead Data Architect – Qualified Pipeline Location: Remote, US based Position type: TBD Job Summary Data Meaning is a front-runner in Business Intelligence and Data Analytics consulting, renowned for our high-quality consulting services throughout the US and beyond. Our expertise lies in delivering tailored solutions in Business Intelligence, Data Warehousing, and Project Management. We have a diverse, global team of consultants, all working remotely, embodying a collaborative, inclusive, and innovation-driven work culture. As part of our growth and upcoming initiatives, we are proactively searching for Lead Data Architects to support future data platform modernization initiatives across our client portfolio. Position Summary The Lead Data Architect will lead the architecture assessment, transition planning, and modernization of the client’s data platform. This role will be responsible for designing the future-state architecture, defining best practices for ELT pipelines, and guiding the migration of existing SQL-based transformations into a scalable dbt framework within a Snowflake environment. The position will work closely with the client’s engineering teams and internal Data Meaning consultants, leading knowledge transfer sessions, architecture walkthroughs, and platform transition initiatives to ensure long-term scalability, reliability, and governance of the data platform. Key Responsibilities: - Lead architecture assessment and documentation of the current data platform (Snowflake, Airflow, AWS) - Design the future-state data platform architecture following modern ELT and dbt best practices - Define standards for: data modeling, pipeline architecture, transformation frameworks, governance and data quality - Lead the migration of transformation logic into dbt models - Define architectural patterns for: staging layers, intermediate layers, data marts - Implement automated testing frameworks within dbt - Lead discovery sessions and technical workshops with the client’s data engineering teams - Document architecture, pipelines, and operational processes - Conduct architecture walkthrough sessions during knowledge transfer phases - Define standards for CI/CD, monitoring frameworks, and data quality validation - Establish schema management and governance best practices - Optimize Snowflake compute usage and platform performance - Design scalable orchestration strategies for Airflow pipelines - Improve platform reliability, monitoring, and operational observability Required Skills & Qualifications: - Advanced / fluent English - 10+ years of experience in Data Engineering or Data Architecture - Strong experience designing and implementing modern data platforms - Expert-level experience with Snowflake - Strong experience implementing dbt transformation frameworks - Advanced SQL modeling and performance optimization - Strong experience with Apache Airflow orchestration - Strong Python programming skills - Experience designing: ELT pipelines, Data warehouse architectures, Transformation frameworks, Large-scale data pipelines - Experience working in cloud environments (AWS preferred) Nice to Have - Snowflake certification - Experience leading data platform migrations or modernization initiatives - Experience implementing Medallion architecture patterns - Experience defining data governance frameworks - Experience working with distributed or global engineering teams

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Qualified Health is seeking a Data Engineer to support partner integration. This isn't a role where you'll maintain legacy systems or write reports nobody reads. You'll build the data pipelines that directly power AI systems used by clinicians across major U.S. health systems — systems that help doctors identify the right diagnosis, flag patients who need intervention, and improve outcomes at scale. Every query you write, every pipeline you build, directly touches patient care. You'll work in a pod with a Director who owns the customer relationship and 1-2 peers who share the mission. The path from Senior Data Engineer to Director of your own pod is clear and real — we're growing fast enough that the opportunity is a matter of when, not if. Key Responsibilities - Build and maintain data ingestion pipelines that pull from Epic Clarity databases, FHIR APIs, and HL7 feeds into our Databricks lakehouse architecture. - Execute data quality validation and mapping verification for each new workflow deployment. - Support 2-4 health system partners as part of a dedicated integration pod. - Develop and optimize SQL queries against complex healthcare data models. - Contribute to defining reusable data transformation patterns that accelerate future partner onboarding. Qualifications - 4+ years as a data engineer building production ETL/ELT pipelines. - Strong SQL skills (complex joins, window functions, CTEs across large healthcare datasets). - Proficiency with Python and PySpark for data transformation at scale. - Experience with cloud data platforms (Databricks preferred; Snowflake, BigQuery, or Redshift accepted). - Familiarity with FHIR data models or willingness to learn healthcare data standards quickly. - Azure cloud services experience (ADLS Gen2, Key Vault, networking basics). - Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field. Desired Skills - Epic Clarity/Caboodle experience or healthcare data background. - Delta Lake, Delta Sharing, or lakehouse architecture experience. - HL7 message processing or real-time healthcare data pipelines. - Experience working in a multi-tenant SaaS environment. Technical Environment - Azure Databricks + Data Factory (plus Fabric and Snowflake integrations). - PySpark for distributed data processing. - GitHub Actions + Terraform for CI/CD and Infrastructure as Code. - Python with type-safe patterns and modern frameworks. - Healthcare data formats including FHIR, Epic Clarity, and other EHR schemas. What Success Looks Like - High-quality data pipelines delivered on schedule with thorough testing and documentation. - Proactive issue identification with technical problems caught and resolved before impacting partners. - Reusable components that reduce implementation time for subsequent integrations. - Clean production deployments with minimal post-launch issues. - Technical credibility with partner IT teams based on quality of work. - Efficient troubleshooting with quick diagnosis and resolution of data quality issues. Impact & Growth Opportunity As a Data Engineer at Qualified Health, you'll build the data infrastructure that powers AI-driven insights for major health systems. Your work directly enables better patient care by ensuring high-quality, reliable data flows into clinical decision support tools. This role offers deep technical learning in healthcare data, exposure to diverse health system architectures, and growth potential into senior technical or platform architecture roles as we scale. Why Join Qualified Health? - Mission that matters: We partner with the country's leading health systems to safely deploy AI that improves patient care, operational efficiency, and financial performance. - Serious traction: 14+ health system partners including the University of Texas system, University of Rochester Medical Center and Jefferson Health. - Public Benefit Corporation: We're organized to prioritize patient outcomes alongside business performance. - Comp that competes: We offer market competitive base salary, meaningful equity with real upside, and comprehensive benefits. Our Commitment to Diversity Qualified Health is an equal opportunity employer. We believe that a diverse and inclusive workplace is essential to our success, and we are committed to building a team that reflects the world we live in. Pay & Benefits The pay range for this role is between $155,000 and $190,000, and will depend on your skills, qualifications, experience, and location. This role is also eligible for equity and benefits. Join our mission to revolutionize healthcare with AI. To apply, please send your resume through the application below.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to shape and lead the organization’s enterprise data strategy, bridging technical architecture, governance, and team leadership. You will: - Design and maintain robust data pipelines. - Establish a unified enterprise data model. - Enable AI-ready analytics while ensuring data quality and trustworthiness. - Drive impact across multiple functions, including Sales, Marketing, Finance, Delivery, and Product, by providing accurate, actionable insights. - Lead a team of data engineers and analysts. - Mentor stakeholders on data stewardship. - Influence enterprise-wide data governance practices. - Combine hands-on engineering, strategic planning, and cross-functional collaboration in a dynamic, growth-focused environment. - Operate effectively across locations while leading a high-performing data organization. Qualifications - 10+ years of experience in data engineering, including people leadership or functional leadership of data/reporting teams. - 4+ years of hands-on Snowflake expertise, including warehouse design, RBAC, performance tuning, and cost management; Snowflake certification required. - Advanced SQL and proficiency in Python or similar data-oriented languages; experience with dbt, Matillion, Fivetran, Airflow, Prefect, or equivalent tools. - Proven experience leading enterprise reporting, analytics engineering, or data governance initiatives. - Strong knowledge of semantic layers, AI/ML-enabled analytics, and building AI-ready data solutions. - Excellent communication skills with the ability to influence technical and non-technical stakeholders in a matrixed environment. - Preferred: experience in SaaS, education, or services delivery; prior participation in enterprise data strategy or governance councils. Requirements - Architect, build, and maintain scalable ELT/ETL pipelines into Snowflake, ensuring optimized performance, storage, and cost efficiency. - Design enterprise data models, semantic layers, and AI-ready data structures that support reporting, forecasting, and analytics. - Lead and mature the data engineering and reporting team, providing coaching, setting standards, and managing project delivery priorities. - Establish and enforce data governance practices, MDM standards, and data quality controls to ensure consistent, reliable, and trustworthy data. - Partner with cross-functional teams to translate business needs into governed, scalable, and actionable data solutions. - Monitor and optimize pipeline performance, implement automated validation frameworks, and support continuous improvement initiatives. - Drive enterprise adoption of data stewardship, ensuring practical governance practices are embedded into daily workflows. Benefits - Competitive salary range: $135,000–$175,000, based on experience, skills, and internal equity. - Comprehensive health, dental, and vision coverage; 401(k) and paid time off. - Flexible remote work arrangements and professional development opportunities. - Supportive, collaborative environment fostering growth, innovation, and impact. - Opportunity to lead high-visibility initiatives that directly influence organizational success and AI-driven decision-making.