Sr. Data Architect

LG Energy Solution Michigan, Inc.•Westborough, MA

2d•$150,000 - $190,000•Remote

About The Position

The Senior Data Architect is responsible for designing and operating Vertech's centralized data foundation, enabling trusted analytics, reporting, and AI solutions across the enterprise. This role serves as the architect of the data layer that sits between operational systems and business-facing data products. The role transforms fragmented and complex source data into a trusted and scalable foundation that can be consistently leveraged across the organization. The position plays a key role in expanding the breadth and maturity of the centralized data platform by incorporating new data sources, establishing common business definitions, and developing the semantic layer that enables consistent interpretation of data across reporting, analytics, and AI solutions. Success in this role is measured by the organization's ability to leverage a common data foundation, reducing dependence on shadow databases, siloed reporting solutions, and competing versions of business data. The Senior Data Architect combines systems thinking with architectural excellence, creating scalable patterns that improve reliability, maintainability, governance, security, performance, and cost management across the enterprise data ecosystem. Snowflake and dbt are the primary technologies supporting Vertech's centralized data platform, and strong experience with both platforms is essential. The role works closely with the Data Products & Enablement Manager, Business Applications, Information Technology, Cybersecurity, business stakeholders, and external partners to ensure enterprise data remains trusted, scalable, and aligned with Vertech's long-term data and AI strategy.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Analytics, Information Systems, or a related technical field.
5+ years of experience in data architecture, data engineering, analytics engineering, business intelligence, or related disciplines.
Strong understanding of data modeling, data architecture principles, and data governance practices.
Strong understanding of semantic layer and context layer frameworks.
Advanced SQL skills and experience working with modern cloud data platforms.
Experience designing scalable and maintainable data structures that support reporting, analytics, and AI use cases.
Strong understanding of metadata management, master data concepts, role-based access controls, and enterprise data standards.
Demonstrated ability to simplify complex information and communicate technical concepts effectively to business stakeholders.
Strong analytical, documentation, communication, and problem-solving skills.

Nice To Haves

Experience with Snowflake and dbt.
Experience developing semantic layers, governed metrics, or enterprise reporting frameworks.
Familiarity with data governance frameworks, data catalog solutions, or stewardship programs.
Experience supporting AI and machine learning use cases through data preparation and architecture.
Experience within renewable energy, manufacturing, field service, or asset-intensive industries.
Relevant Snowflake, Microsoft, DAMA, or data architecture certifications.

Responsibilities

Design and evolve scalable data models and foundational data structures that support analytics, reporting, and AI initiatives used by data products, data practitioners, and data consumers.
Transform siloed source-system data into standardized, cohesive, business-friendly datasets that can be easily understood and consumed by technical and non-technical users.
Design and maintain semantic models, business definitions, metrics, hierarchies, and context layers that ensure consistent interpretation of data across reporting and AI platforms.
Expand the centralized data platform by identifying, prioritizing, and onboarding new data sources and incorporating additional business systems and datasets into the shared data model.
Prepare enterprise data assets for AI and advanced analytics use cases through improved structure, metadata management, contextual relationships, documentation, quality controls, and governance practices.
Develop scalable frameworks for Snowflake role design, access controls, and data-sharing models that balance usability, security, cost tracking, and long-term maintainability.
Define standards and controls that improve trust, consistency, accuracy, completeness, and reliability across enterprise data assets.
Identify opportunities to reduce complexity, eliminate redundancy, improve maintainability, and support long-term growth of enterprise data assets.
Create and maintain technical documentation, data dictionaries, architecture diagrams, semantic definitions, governance artifacts, and development standards.