Data Engineer
Coloplast
Minneapolis, MN, US
Hybrid
2026-06-16
Announced salary
$87,048 - $130,572
Low
$91K
Median
$119K
High
$156K
Market in Minneapolis · BLS OEWS 2025
Estimated net pay
$5,472 - $7,758
/month · 25% withheld
after tax & contributions · Single, no dependents
Job description
**Location:**Minneapolis, MN, US
**Job Family:** Information Technology
**Country/Region:** United States
As a Data Engineer, you will have the unique opportunity to design and establish a modern data engineering function within our organization. You will take the lead in building the data infrastructure that enables analytics, self\-service reporting, and AI\-ready data systems across the organization with Microsoft Fabric as a core platform. While you will operate with autonomy, you will also partner closely with data analysts, business stakeholders, development teams, and IT to modernize our hybrid on\-premises and cloud data environment.
This is a hands\-on role with strategic influence—you will own the full engineering lifecycle, from assessing our current data landscape and identifying gaps, to designing and implementing pipelines, data warehouses, Fabric Lakehouses, and standardized data products. You will have the opportunity to define best practices, establish patterns, and shape the infrastructure and culture of data engineering within Comfort Medical. Success in this role will require strong technical fundamentals, the ability to work independently while collaborating effectively across teams, and a forward\-thinking approach to building systems that scale with business needs.
**Major Areas of Accountability**
**Data Infrastructure \& Pipeline Development**
* Assess the current data landscape, identify infrastructure gaps, and propose solutions aligned with business needs and technical best practices.
* Design and implement ETL/ELT pipelines including Microsoft Fabric, Synapse Spark, or modern equivalent platforms to build and maintain data warehouses, Fabric Lakehouses, and lakehouse architectures that serve analytics, self\-service reporting, and AI use cases.
* Establish data quality standards and SLAs for availability, quality, and timeliness through automated validation, testing, monitoring, alerting, and incident response processe
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