Senior Data Engineer

Senior Data Engineer – Enterprise Data Modeling
Location: Remote – U.S. | Phoenix, AZ candidates preferred
Compensation: $110,000–$160,000
Employment: Full-time
About the Company
A large, established organization is making a significant investment in modernizing its enterprise data platform and creating a more standardized, scalable foundation for analytics across the business.
The organization is building a modern layered data architecture and expanding the curated core of its platform. As part of this transformation, the team is establishing enterprise data modeling as a formal engineering discipline and creating reusable models that can support multiple business domains and use cases.
This creates an opportunity for an experienced Senior Data Engineer to have significant influence over how the enterprise data foundation is designed and built.
About the Role
We’re looking for a hands-on Senior Data Engineer who combines strong Data Engineering capabilities with deep expertise in enterprise data modeling.
This is not a standalone Data Modeler position.
You’ll remain hands-on as an engineer while leading the modeling effort across shared enterprise business domains. You’ll work from initial business requirements and source-system analysis through conceptual, logical, and physical modeling—and help implement those models within the data platform.
The emphasis is on building canonical enterprise models that can support multiple use cases, rather than creating models for one application, dashboard, or reporting requirement.
Because the enterprise modeling practice is still developing, you’ll also have the opportunity to establish standards, influence architecture, improve existing models, and coach other Data Engineers on modeling best practices.
What You’ll Do
Lead conceptual, logical, and physical data modeling across shared enterprise business domains.
Design canonical enterprise models capable of supporting multiple applications, analytics requirements, and business use cases.
Partner with business stakeholders to understand requirements, processes, terminology, and relationships between core business entities.
Translate complex business requirements into scalable, well-structured enterprise data models.
Perform hands-on data analysis, profiling, and source-system assessment before designing models.
Work across normalized, dimensional, and Data Vault modeling approaches.
Build and implement dimensional models, including facts and dimensions, for analytics and reporting.
Support downstream analytics environments including Power BI.
Contribute to existing enterprise models while designing and implementing new ones.
Establish modeling standards, naming conventions, documentation practices, and model-review processes.
Use enterprise data modeling tools to develop and maintain conceptual, logical, and physical models.
Partner with Data Engineers, Architects, BI Developers, Business Analysts, and Governance teams.
Collaborate on business definitions, data lineage, data quality, and cataloging.
Guide and coach other Data Engineers on modeling approaches and standards.
Help establish the curated enterprise layer within a modern data architecture.
Must Haves
Strong hands-on Data Engineering experience combined with deep data modeling expertise.
Proven experience leading conceptual, logical, and physical data modeling.
Experience designing models across shared enterprise business domains rather than exclusively for individual applications or reports.
Strong experience with normalized, dimensional, and Data Vault modeling approaches.
Experience translating business requirements into scalable enterprise data models.
Hands-on data profiling, data analysis, and source-system assessment experience.
Strong dimensional modeling experience, including designing and implementing facts and dimensions.
Experience supporting analytics and BI use cases, including environments using Power BI.
Experience with an enterprise data modeling tool such as sqlDBM, Erwin, ER/Studio, or similar.
Experience establishing or enforcing data modeling standards, naming conventions, and model-review practices.
Ability to personally implement models as a hands-on Data Engineer rather than operating exclusively at the architecture or design level.
Experience collaborating with Data Engineers, Architects, BI Developers, Business Analysts, and business stakeholders.
Experience partnering with data governance or catalog teams around business definitions, lineage, and data quality.
Ability to coach other engineers and raise the overall level of data modeling capability across a team.
Nice to Have
dbt experience.
Experience working within a Medallion or similar layered architecture across Bronze, Silver, and Gold layers.
Experience building out a curated/Silver enterprise data layer.
Familiarity with Master Data Management concepts.
Informatica IICS or PowerCenter experience.
AWS experience.
Experience migrating data environments from on-premises SQL Server to the cloud.
Git-based development workflows.
Experience with data governance or catalog platforms such as Data.world.
Experience within logistics, field services, waste, transportation, or other asset-heavy operational environments.
What Makes Someone Successful Here
The strongest candidate will be able to operate equally well in a business conversation, a modeling session, and a hands-on engineering environment.
You can sit with business stakeholders, understand how concepts such as customers, facilities, products, operations, and financial entities relate to one another, and translate those relationships into well-designed conceptual and logical models.
From there, you can take the work further—profiling the underlying source data, designing the physical model, and helping implement it within the modern data platform.
Most importantly, you think at the enterprise level.
Rather than designing a model to satisfy one dashboard or application, you think about how standardized entities and relationships can serve multiple teams and use cases across the organization.
You’ve ideally worked in an environment where enterprise modeling was still developing and helped establish the standards, processes, and engineering discipline needed to scale it.
Why Join?
This is an opportunity to have meaningful ownership over an enterprise data platform while it is still being built.
You’ll help define the curated core of the platform, establish enterprise modeling standards, and create reusable data models that become foundational assets across the organization.
You’ll have the opportunity to remain hands-on while also influencing architecture, standards, and the development of other Data Engineers.
For someone who combines Data Engineering + enterprise data modeling + hands-on implementation, this role provides the opportunity to build rather than simply inherit a mature environment.

Work arrangement
Yes