Data Engineering Manager

SmarterDx is seeking a Manager of Data Engineering, Development to lead and grow a high-performing data engineering team responsible for building next-generation data pipeline capabilities, architecting scalable data solutions, and developing new data products to power our clinical AI applications. You will drive innovation in our data stack and data pipelines, establish technical best practices, and ensure robust, observable, and cost-effective data architectures. You will set clear objectives, mentor engineers, as well as partner closely with Product, Clinical, Data Science, and Software Engineering teams to deliver transformative data capabilities.

SmarterDx builds clinical AI that empowers hospitals to analyze the complete record of every patient to fully capture the value of care delivered. Founded by physicians in 2020, its proprietary AI platform understands the nuances of clinical reasoning, enabling hospitals to true the patient record for every discharge. By doing so, hospitals can recover millions in earned revenue, enhance care quality metrics, and optimize healthcare operations. Become a Smartian and help optimize the way the healthcare system works for everyone. Learn more at smarterdx.com/careers.

**This role is fully remote within the US**

• →Lead, coach, and develop a team of senior data engineers with a focus on technical innovation, platform capabilities, and engineering excellence.
• →Contribute to the data engineering product roadmap by designing and implementing new data pipeline architectures, patterns, and reusable frameworks.
• →Architect scalable data solutions using modern tooling (dbt, Airflow, Snowflake, AWS, Python) to enable advanced analytics and ML capabilities.
• →Establish development standards and best practices including code reviews, testing frameworks, CI/CD pipelines, and documentation standards.
• →Develop sophisticated data models and transformations using advanced dbt patterns (custom macros, packages, incremental models) to enhance our production pipelines
• →Collaborate cross-functionally to help translate business requirements into technical solutions and drive innovation initiatives.
• →Develop automated data quality frameworks, monitoring solutions, and observability tools to ensure data reliability at scale.
• →Create developer productivity tools and accelerators that enable the team to deliver features faster with higher quality.
• →Drive technical design reviews, architectural decisions, and technology evaluations to ensure platform scalability and maintainability.

• 7+ years of data engineering experience, including leadership of engineers and technical projects (architecture, design, delivery).
• Deep expertise with modern data stack: advanced dbt development, Snowflake optimization, Python, and SQL at scale.
• Strong expertise with AWS cloud architecture skills: hands-on experience in serverless and infrastructure-as-code patterns.
• Proven track record building and scaling data platforms that support analytics, ML, and product features.
• Developer excellence mindset with expertise in Git, CI/CD, testing, code reviews, and agile development practices.
• Demonstrated ability to drive technical innovation while balancing technical debt, performance optimization, and feature delivery.
• Excellent communication and technical leadership skills: comfortable driving architectural decisions and mentoring senior engineers.
• Experience in healthcare datasets with understanding of the inherent contextual and relational complexities within healthcare data.

• Experience at a scale-up or rapid-growth technology company.
• Advanced Snowflake features expertise (streams, tasks, dynamic tables, data sharing).
• Infrastructure-as-code proficiency (AWS CDK, CloudFormation, Terraform).
• ML engineering experience: feature stores, model deployment, MLOps practices.
• Healthcare interoperability knowledge (HL7, FHIR, CCD/C-CDA).
• Certifications: Snowflake (SnowPro Advanced), AWS (Data Analytics or Solutions Architect Professional), dbt, or healthcare data (CHDA/RHIA).
• Experience building data mesh architectures or domain-driven data platforms.

• Databases: Snowflake (primary), Postgres
• Cloud Infrastructure: AWS (CDK, S3, MWAA, EventBridge, Batch, Lambda, Step Functions), FiveTran
• Technologies: SQL, dbt, Python