This is a remote position; however, the candidate must reside within 30 miles of one of the following locations: Portland, ME; Boston, MA; Chicago, IL; San Francisco Bay Area, CA; and Seattle/WA.
About the Role/Team
The Semantic Data Modeling Layer is responsible for translating raw, domain-ingested data into structured, semantically meaningful, and trusted data entities that represent core business concepts.
As a Senior Software Engineer, you'll play a key role in designing , building, and scaling our semantic data layer that powers analytics, business intelligence, and data-driven decision-making across the organization. This role will be central to enabling a consistent, governed, and reusable definition of metrics, dimensions, and business logic that can be consumed across tools, platforms, and teams. Your work will be critical in creating a single source of truth, consolidating fragmented data sources into unified, reconciled views.
How you'll make an impact
Build modular, reusable semantic definitions of business entities, metrics and hierarchies.
Implement business rules, calculations, and aggregations in the semantic layer.
Establish data governance principles to ensure consistency and metrics definitions are standardized and compliant.
Define and implement robust data modeling solutions, ensuring data quality, consistency, and interoperability across the organization.
Implement validation, testing, and monitoring of semantic models for accuracy and reliability.
Partner closely with the data products team to understand business requirements and ensure semantic models align with their needs.
Participate in code reviews, design sessions, and incident resolution—promoting high standards for code quality and operational reliability.
Experience you'll bring
4–8 years of experience as a software or data engineer, ideally in high-volume or distributed systems environments.
Strong programming skills in Python, Java, or another backend language for data services.
Strong SQL skills and experience with modelling large-scale, complex datasets.
Solid grasp of engineering fundamentals, including version control, modular design, testing, and performance tuning.
Proven experience with at least one modern cloud data platform (Snowflake, BigQuery, Databricks)
A collaborative mindset—comfortable working across domains, products, and infrastructure layers.
A strong sense of ownership and accountability—you care deeply about building systems that last.