Senior Manager, Data Engineering

Fuze Health is dedicated to enhancing quality and value in healthcare through technology. As the Senior Manager of Data Engineering, you will lead the strategy and execution of Alto's data platform, ensuring high-quality data supports analytics and product innovation while managing a high-performing team of Data Engineers.

Responsibilities:

• Lead, mentor, and grow a team of Data Engineers, fostering a culture of ownership, technical excellence, and continuous improvement
• Define and execute the long-term vision for Alto’s data platform, ensuring alignment with company strategy and product roadmaps
• Partner with Product, Engineering, Data Science, Analytics, Finance, and Enterprise teams to prioritize and deliver high-impact data initiatives
• Establish clear career paths, performance standards, and hiring plans to scale the team thoughtfully and sustainably
• Build strong stakeholder trust by translating business needs into scalable data solutions
• Own the design and evolution of Alto’s data architecture, including ingestion, transformation, orchestration, storage, and serving layers
• Administer and maintain core platform tooling — including Fivetran, Airflow, dbt, Snowflake, and Looker — ensuring these systems operate as reliable, scalable, and secure infrastructure
• Provide a robust, well-governed platform foundation that enables Analytics to build and manage transformation logic within dbt, while enforcing standards for performance, testing, deployment workflows, access controls, and warehouse efficiency
• Ensure the underlying data platform reliably powers analytics, operational workflows, experimentation, and machine learning use cases — delivering trusted, well-documented, and production-grade data assets to both internal stakeholders and downstream systems
• Ensure the reliability, performance, and freshness of data pipelines through strong observability, lineage tracking, testing frameworks, and operational rigor
• Scale the platform to support increasing data volume, near-real-time use cases, and expanding business and regulatory complexity
• Evolve and strengthen existing standards for data governance, access control, privacy, and compliance within a regulated healthcare environment
• Refine and operationalize SLAs and performance metrics for data pipelines and platform reliability, ensuring clear accountability and transparency
• Enhance and scale current data quality monitoring and incident management practices to improve resilience, observability, and response times
• Continuously optimize infrastructure performance and cost efficiency through thoughtful architectural improvements and warehouse tuning
• Evaluate emerging technologies and evolve Alto’s data stack to support streaming, near-real-time analytics, and scalable AI workflows
• Enable robust experimentation and measurement by providing reliable, well-instrumented, and production-grade data pipelines that Analytics, Data Science, and Product teams can confidently build upon
• Collaborate closely with Data Science and Machine Learning teams to ensure the platform scales alongside growing model and feature complexity, providing the infrastructure, environment consistency, and operational guardrails necessary for reliable training and production inference
• Drive adoption of best-in-class tooling and practices that improve developer velocity and reduce operational toil
• Cultivate a culture where data is treated as a product — with clear ownership, discoverability, and accountability

Requirements:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; advanced degree preferred
• 10+ years of experience in data engineering, platform engineering, or related data infrastructure roles
• 5+ years of experience leading and scaling high-performing technical teams
• Proven experience architecting, operating, and scaling modern cloud-based data platforms, including data ingestion, orchestration, cloud data warehouses, transformation frameworks, and semantic serving layers
• Strong expertise in data modeling principles, ELT architectures, and production-grade pipeline orchestration
• Hands-on experience with cloud environments (AWS, GCP, or Azure)
• Proven track record owning platform reliability, including defining SLAs, implementing observability and data quality frameworks, and leading incident response and postmortems for production data systems
• Experience operating in high-growth, fast-paced technology organizations
• Strong communication skills with the ability to influence technical and non-technical stakeholders alike
• Experience building data platforms in healthcare, pharmacy, fintech, or other regulated industries
• Familiarity with HIPAA and healthcare data privacy standards
• Experience supporting machine learning pipelines, feature engineering workflows, or feature stores
• Experience with streaming architectures (e.g., Kafka, Kinesis, Pub/Sub) and real-time analytics use cases
• Exposure to experimentation platforms and product analytics ecosystems
• Strong SQL and programming proficiency (e.g., Python, Scala, or similar)
• Experience implementing data contracts and data product frameworks across domain teams