BI Engineer

Company Overview

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 on 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.

The Data Engineer will help build and support the data foundation that powers enterprise reporting, analytics, and operational decision‑making across Spire. This full‑time role focuses on creating reliable data pipelines, well‑structured data models, automated quality checks, and documented datasets that analytics, finance, operations and revenue cycle teams can trust. Salary range: $113,200 - $169,800.

Location:

Stamford, CT 06905.

• 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 alerts 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.

• 4+ years of experience in data engineering, analytics engineering, business intelligence engineering, or a closely related technical data role.
• Strong SQL skills, including complex joins, window functions, CTEs, query optimization, data validation, and performance tuning.
• Hands‑on experience with Snowflake or another modern cloud data warehouse.
• Experience building ELT/ETL pipelines and transforming raw source data into modeled, analysis‑ready datasets.
• Proficiency with Python for automation, data processing, validation, or pipeline support.
• Experience with dbt, Airflow, Dagster, Fivetran, Matillion, Azure Data Factory, or comparable data transformation/orchestration tools.
• Understanding of dimensional modeling, semantic layers, data marts, and structures that support reliable BI reporting.
• Working knowledge of Power BI datasets, Power Query, DAX, or BI consumption patterns, even if dashboard development is not the primary focus.
• Comfortable working with messy, high‑volume, multi‑source data and turning it into clean, trustworthy, documented outputs.
• Strong problem‑solving skills and the ability to troubleshoot data issues from source extraction through final report output.
• Clear communication skills, including the ability to explain technical issues and tradeoffs to non‑technical stakeholders.
• High attention to detail, strong ownership, and a low tolerance for…