Data Engineering Manager, SmarterDx

• 7+ years of data engineering experience, including leadership of technical ICs and projects (architecture, design, delivery)
• Deep experience with health care datasets: clinical and/or billing/claims data; familiarity with HL7/FHIR and interoperability patterns
• Strong expertise with modern data stack: advanced dbt development, Snowflake operations, Python, and SQL at scale
• Proven track record of building in scaled data platforms that support analytics, ML, and product features
• Developer and operational excellence mindset: expertise in Git, CI/CD, testing, SLOs/SLAs, incident management, and agile practices
• Excellent communication and stakeholder management; comfortable driving design and implementation decisions to create alignment across technical and non-technical partners
• Willingness to roll up sleeves and contribute hands-on to unblock the team

Desirable

• SmarterDx is seeking a Manager of Data Engineering to lead and grow a high-performing data engineering team
• You will be responsible for building scalable data pipeline capabilities, robust data solutions, and ensuring reliable ingestion and harmonization of health care data (EHR, clinical, and claims) across our products
• You will drive innovation in our data stack, establish technical best practices, and ensure resilient, observable, and cost-effective data pipelines
• You will set clear objectives, mentor engineers, and partner closely with Product, Clinical, Data Science, and Software Engineering teams to deliver transformative data capabilities
• Lead, coach, and develop a team of data engineers with a focus on technical excellence, delivery velocity, and collaborative culture
• Lead a teams to design and maintain robust data models that accurately represent complex clinical and billing data across diverse EHR systems
• Create scalable data solutions using modern tooling (dbt, Airflow, Snowflake, AWS, Python) to enable advanced analytics and ML capabilities
• Design and implement data pipelines, reusable patterns, and frameworks that accelerate delivery and ensure reliable operation
• Establish development standards and best practices including code reviews, testing frameworks, CI/CD pipelines, and documentation standards
• Implement comprehensive data quality and testing frameworks (schema, nullability, referential integrity, reconciliation) and lineage observability
• Establish and enforce SLOs/SLAs, instrument critical paths, and drive incident reduction via proactive monitoring, alerting, and post-incident reviews
• Collaborate cross-functionally (Product, Clinical, Data Science, Software Engineering) to translate business requirements into technical solutions and drive measurable outcomes
• Drive technical design reviews, architectural decisions, and technology evaluations to ensure platform scalability and maintainability