Analytics Engineer: Microsoft Fabric Data Modeling (Healthcare)

tactilemedical; Minneapolis (MN); On-site; USD 94,000 - 131,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401(k)

Paid time off

Parental leave

Life insurance

Job summary

Tactile Medical is seeking an Analytics Engineer to develop analytics-ready data models in Microsoft Fabric and support governance-compliant datasets. You will transform ingested data into trusted analytical assets under guidance from senior engineers.

Responsibilities include building curated datasets, applying dimensional modeling, and ensuring data quality for self-service analytics across the organization.

Qualifications

• 3–5 years in data analytics, data engineering or related field.
• Ability to design and implement data solutions with SQL.
• Work directly with business stakeholders to gather requirements and deliver data solutions.
• Hands-on experience developing analytics, reporting or data solutions.

Responsibilities

• Build and maintain curated analytic datasets using Microsoft Fabric.
• Apply dimensional modeling patterns under guidance.
• Contribute to centralized models and metric definitions.
• Ensure reuse and consistency across analytics use cases.
• Validate data against business expectations and benchmarks.
• Collaborate with BI developers to meet reporting needs.
• Identify and escalate downstream data issues related to modeling.
• Improve dataset clarity with consistent naming and structure.
• Follow standards set by Data Architect and Analytics leads.
• Align datasets with governance requirements and documentation.

Skills

SQL

DAX

Power Query

Microsoft Fabric

Data Modeling

Tools

Power Query (M)

Microsoft Fabric

Job description

Tactile Medical is seeking an Analytics Engineer to develop analytics-ready data models in Microsoft Fabric and support governance-compliant datasets. You will transform ingested data into trusted analytical assets under guidance from senior engineers.

Responsibilities include building curated datasets, applying dimensional modeling, and ensuring data quality for self-service analytics across the organization.