Senior Analytics Engineer

AICA Orthopedics, P.C.•Atlanta, GA

About The Position

We are seeking a Senior Analytics Engineer to serve as a senior technical contributor and long-term owner of analytical data models and source-of-truth logic within our Azure-based data platform. This role will work closely with the data platform and integration efforts built on Azure Data Factory, Azure Data Lake Storage Gen2, and SQL-based serving layers, with an immediate focus on healthcare data integration from our Enterprise EHR, Financial Systems, Marketing Platforms, and Custom Apps. This position does not function as the overall data platform or infrastructure lead. Instead, it serves as the analytics engineering authority embedded within the platform, responsible for ensuring data is modeled correctly, business logic is encoded consistently, and the warehouse reliably supports clinical, financial, and operational decision-making.

Requirements

5–8+ years of experience in analytics engineering, data engineering, or related roles.
Expert SQL skills with experience designing analytical data models.
Experience working with modern cloud data warehouses.
Hands-on experience with Azure Data Factory, Azure Data Lake Storage Gen2, and SQL-based analytics layers.
Experience integrating complex source systems such as EHRs, ERPs, and CRMs.
Experience implementing data quality frameworks and documentation practices.
Comfortable operating with autonomy and technical ownership.

Nice To Haves

Experience working with healthcare data including EHR, encounters, claims, and revenue cycle.
Familiarity with healthcare data complexity and governance considerations.
Experience supporting multi-clinic or multi-provider environments.

Responsibilities

Partner with the data platform team to understand, contribute to, and support the Azure-based data lake and warehouse architecture.
Build and maintain ELT pipelines from core systems including EHR Systems, CRM/Marketing Systems, Case Management Systems, and custom applications.
Define and maintain canonical data models including patients, encounters, providers, revenue cycle, claims, and marketing funnel entities.
Establish standards for analytical modeling, transformation layers, and source-of-truth definitions.
Own the transformation layer using SQL-first modeling frameworks such as DBT or equivalent.
Encode business rules and metric definitions into deterministic transformation logic.
Implement automated data quality and validation tests including freshness, completeness, and consistency checks.
Maintain clear documentation describing data lineage, definitions, and usage guidance.
Partner closely with the Analytics Lead / BI Manager and Azure data platform stakeholders.
Work with Finance, Clinical, and Operations teams to resolve data definition conflicts.
Support ad-hoc data investigations during critical decision cycles.
Enable analysts and business users to safely and effectively leverage the warehouse.