Data Engineering Manager

SmarterDx is seeking a Manager of Data Engineering, Integrations to lead and grow a high‑performing data engineering team responsible for ingesting, harmonizing, and operationalizing healthcare data (EHR, clinical, and claims) across our products. You will scale new‑client onboarding, raise the bar on data quality and governance, and ensure resilient, observable, and cost‑effective ELT pipelines. You will set clear objectives, mentor engineers, and partner closely with Product, Clinical, Data Science, and Engineering to deliver durable outcomes.

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 analytics engineers and/or data engineers with a focus on technical excellence, delivery velocity, and collaborative culture.
• →Ensure on-time delivery of client data integrations by owning enterprise data model standards and maintaining consistent, governed data definitions.
• →Oversee client data pipelines using modern tooling (dbt, Airflow, Snowflake, AWS, Python) to ensure reliable operation and uptime.
• →Drive client integration delivery speed by implementing automation, reusable patterns, and clear runbooks to directly optimize client onboarding timelines.
• →Establish and enforce SLOs/SLAs, instrument critical paths, and drive incident reduction via proactive monitoring, alerting, and post‑incident reviews.
• →Implement comprehensive data quality and testing frameworks (schema, nullability, referential integrity, reconciliation) and lineage observability.
• →Collaborate cross‑functionally (Product, Clinical, Data Science, Engineering) to prioritize roadmaps, land measurable outcomes, and manage tradeoffs.
• →Manage high-volume client integration requests, triaging and prioritizing tickets based on client impact, urgency, and business value while maintaining clear communication on resolution timelines.
• →Own daily operations rhythm including ticket review, resource allocation, escalation management, and stakeholder updates to ensure client integration commitments are met.
• →Drive operational metrics and reporting on integration delivery performance, ticket resolution times, and team capacity to identify bottlenecks and improve client outcomes

• 7+ years of data engineering experience, including leadership of engineers and outcomes (hiring, mentoring, performance, delivery).
• Deep experience with healthcare datasets: clinical, and/or billing/claims; familiarity with HL7/FHIR and interoperability patterns.
• Strong SQL and data modeling expertise; proven track record building reliable ELT in dbt, Airflow, Snowflake, and Python.
• An Operational excellence mindset that integrates SLOs/SLAs, on‑call/incident management, root‑cause analysis, and preventive improvements.
• Demonstrated ability to accelerate client data onboarding through automation, standardization, and repeatable integration patterns.
• Excellent communication and stakeholder management; comfortable driving clarity and alignment across Product, Clinical, and technical partners.
• Willingness to roll up sleeves and contribute hands‑on to unblock the team.

• Experience at a scale‑up or rapid‑growth technology company.
• Experience leading product implementations and complex client data integrations.
• Hands‑on interoperability experience (HL7, FHIR, CCD/C-CDA), EHR extract patterns, and healthcare data normalization.
• Certifications: Snowflake (SnowPro), AWS (Data Analytics or Solutions Architect), dbt, or healthcare data (CHDA/RHIA).

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