This U.S. based leader in grid-scale battery storage is positioned to become one of the dominant companies in the American energy market. Backed by a global clean energy powerhouse, the company combines advanced engineering expertise with cutting-edge manufacturing capacity and a rapidly expanding domestic footprint.
With a 35+ GWh project pipeline and more than 230 professionals already on board, the business is scaling quickly as it continues to develop and deliver some of the industry's most advanced Battery Energy Storage Systems.
As the company grows, it is investing significantly in its data infrastructure and AI capabilities, creating an opportunity to shape how the organization structures, accesses, and leverages data.
The Role
As Senior Data Architect, you will own the company's enterprise data architecture, building a centralized data foundation that supports business intelligence, advanced analytics, and the deployment of AI solutions across the organization.
This strategic, high-impact position focuses on solving complex data challenges at the enterprise level. You will identify architectural limitations, establish scalable data structures, and ensure information from multiple business systems can be consistently accessed, understood, and used.
A major focus will be developing semantic layers that enable AI agents and applications to interact reliably with enterprise data. As the business expands its use of AI across departments, you will play a critical role in ensuring these solutions can access accurate, governed, and contextually meaningful information.
Snowflake is the company's primary data platform, alongside dbt, and you will shape how this environment evolves. We will also consider candidates with extensive architectural experience in Databricks, provided they demonstrate a strong understanding of modern data platforms and semantic modeling.
You will work closely with senior leadership, technology teams, and business stakeholders to define the long-term data strategy while staying hands-on enough to drive technical decisions and implementation.
This is not a traditional data engineering role focused primarily on building pipelines or maintaining existing infrastructure. Instead, you will be the technical authority responsible for addressing high-level architectural challenges and laying the data foundations needed to support the company's broader AI ambitions.
Responsibilities Include
Leading the design and evolution of the company's centralized enterprise data architecture
Developing scalable data models and semantic layers that support analytics, reporting, and AI applications
Establishing the data structures, business definitions, and contextual relationships required to enable AI agents across multiple business functions
Taking architectural ownership of the Snowflake environment, ensuring scalability, performance, security, and long-term maintainability
Identifying and resolving complex architectural challenges associated with fragmented data, inconsistent definitions, and disconnected business systems
Defining the technical strategy for integrating new data sources into a centralized, governed data platform
Working closely with AI and technology teams to ensure enterprise data is structured and accessible for emerging AI use cases
Establishing data governance frameworks, access controls, and quality standards across the organization
Driving the development of reusable data models and standardized business metrics to eliminate competing versions of information
Collaborating with business stakeholders to translate complex operational requirements into scalable technical solutions
Providing technical leadership and architectural direction across data engineering, analytics, and AI initiatives
Supporting the ongoing development of the company's data strategy as its operational footprint and AI capabilities expand
The Candidate
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Analytics, or a related technical discipline
5+ years of experience in data architecture, data engineering, analytics engineering, or related disciplines
Proven experience designing and implementing enterprise-level data architectures within complex organizations
Deep technical expertise in Snowflake or Databricks, with Snowflake experience strongly preferred
Strong understanding of semantic layers, semantic views, data modeling, and enterprise business definitions
Experience designing data architectures that support AI applications, natural-language querying, or agent-based solutions
Advanced SQL skills and a strong understanding of modern cloud data platforms
Experience with dbt or similar data transformation and modeling frameworks
Strong knowledge of data governance, metadata management, data quality, and role-based access controls
Demonstrated ability to identify and solve complex, high-level data architecture challenges
Ability to balance long-term architectural strategy with hands-on technical execution
Strong communication skills and the ability to engage with both technical teams and senior business stakeholders
Comfortable operating in a fast-growing environment with significant ownership and autonomy
We will consider candidates from a range of industries, particularly those with experience developing enterprise data platforms within complex, data-intensive organizations.
We are particularly interested in individuals who have modernized fragmented data environments, established enterprise semantic layers, or built the architectural foundations for AI adoption.
