BI Engineer

Who we are:

Spire Orthopedic Partners is a growing national partnership of orthopedic practices that provides the support, capital and operational resources physicians need to grow thriving practices for the future. As a Management Services Organization (MSO), Spire provides the infrastructure for administrative operations that allows practices to operate at their highest level, so doctors can focus their efforts on what matters most - patient care. Headquartered in Stamford, Connecticut, the Spire network spans the Northeast with more than 165 physicians, 1,800 employees, 285 other clinical providers and 40 locations in New York, Connecticut, Rhode Island and Massachusetts.

What you'll do:

The Data Engineer will help build and support the data foundation that powers enterprise reporting, analytics, and operational decision-making across Spire. This role is responsible for creating reliable data pipelines, well-structured data models, automated quality checks, and documented datasets that analytics, finance, operations, and revenue cycle teams can trust.

This is a technical role first. Healthcare or revenue cycle experience is helpful, but deep RCM expertise is not required. The successful candidate will be comfortable learning the business context while bringing strong engineering discipline to data ingestion, transformation, testing, performance, and production support.

Spire's leadership is actively investing in data and analytics capabilities. This role will help modernize the data environment, improve reporting reliability, and create scalable structures that reduce manual work and make data easier to use across the organization.

Responsibilities/Duties:

• Design, build, and maintain scalable data pipelines that ingest, transform, and publish data from practice management, EHR, billing, finance, and operational source systems.
• Develop and optimize SQL-based data models in Snowflake to support Power BI reporting, executive dashboards, recurring analytics, and ad hoc business analysis.
• Use dbt or similar transformation frameworks to create tested, version-controlled, and documented data models.
• Build data integration processes using APIs, SFTP files, flat files, database extracts, and third-party vendor feeds.
• Create automated data quality checks, reconciliation processes, anomaly detection routines, and alerting to identify issues before they affect downstream reporting.
• Partner with analytics, finance, RCM, and operations stakeholders to translate reporting needs into reliable data structures and reusable datasets.
• Improve data reliability by standardizing definitions, mapping source system fields, documenting lineage, and resolving data inconsistencies across systems.
• Tune query performance, manage large datasets efficiently, and troubleshoot pipeline failures or report performance issues.
• Support secure data handling practices, access controls, and HIPAA-aligned data governance expectations for healthcare information.
• Maintain clear technical documentation, including source-to-target mappings, transformation logic, data dictionaries, and operational runbooks.
• Collaborate using Git-based workflows, code review, and deployment practices appropriate for production data environments.
• Provide production support for scheduled data jobs, recurring reporting datasets, and critical analytics workflows