BI Engineer

Spire Orthopedic Partners•Stamford, CT

2d•$113,200 - $169,800•Onsite

About The Position

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.

Requirements

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 unexplained data discrepancies.

Nice To Haves

Experience in healthcare, physician practice, MSO, revenue cycle, payer, or healthcare operations data environments.
Familiarity with practice management or EHR platforms such as Athena, Modernizing Medicine (ModMed), SIS, or similar systems.
Exposure to healthcare data types such as charges, payments, adjustments, claims, denials, appointments, CPT codes, provider/location hierarchies, or payer data.
Experience working with 835/837 files, ERA data, HL7/FHIR, or other healthcare integration formats.
Experience with cloud platforms such as Azure, AWS, or GCP.
Advanced Excel skills helpful for validation, reconciliation, and stakeholder support.
Bachelor's degree in computer science, data engineering, information systems, analytics, mathematics, statistics, or a related field; equivalent professional experience may be considered.

Responsibilities

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