IT Data Science Principal (Data Warehouse)

Cornerstone Building Brands is a leading manufacturer of exterior building products for residential and low-rise non-residential buildings in North America. Headquartered in Cary, N.C., we serve residential and commercial customers across the new construction and Repair & Remodel (R&R) markets. Our market-leading portfolio of products spans vinyl windows, vinyl siding, stone veneer, metal roofing, metal wall systems and metal accessories. Cornerstone Building Brands’ broad, multi-channel distribution platform and expansive national footprint includes more than 18,800 team members at manufacturing, distribution and office locations throughout North America. Corporate stewardship and Environmental, Social and Governance (ESG) responsibility are embedded in our culture. We are committed to contributing positively to the communities where we live, work and play. For more information, visit us at http://cornerstonebuildingbrands.com.

The IT Data Science Principal provides leadership for enterprise data warehouse and artificial intelligence capabilities, ensuring the organization delivers trusted insights and scalable Data and AI solutions aligned with business strategy. This role leads people, platforms, and delivery practices across analytics engineering, BI, data science/ML, and GenAI enablement.

The Data Warehouse Engineer partners closely with Data & Analytics leadership, Information Security, Risk/Compliance, and business leaders to define priorities, manage teams and vendors, and embed governance for data, models, and AI use cases. This role drives measurable outcomes through roadmap execution, operating rhythms, and continuous improvement across Data Warehouse, analytics and AI delivery.

WHAT YOU’LL DO

• Partner in developing enterprise analytics and AI roadmap, aligning use cases and investments to business strategy, value, and feasibility.

• Establish outcome-based prioritization, intake, and funding recommendations for analytics, ML, and GenAI initiatives.

• Define success metrics and benefits tracking for analytics/AI products and services (adoption, quality, cycle time, and business impact).

• Lead, coach, and partner with multi-disciplinary teams (analytics engineers, BI developers/analysts, data scientists/ML engineers), including vendor/partner resources.

• Set clear expectations for delivery quality, documentation, testing, and responsible AI practices; build development plans and career pathways.

• Build a high-performing organization focused on customer value, operational excellence, and continuous learning across analytics and AI.

• Partner with business and functional leaders to identify, shape, and deliver analytics and AI use cases; translate needs into a clear product backlog and release plan.

• Serve as a trusted advisor on KPI design, experimentation, model performance, and adoption considerations (process, change management, training).

• Communicate priorities, timelines, risk, and decisions clearly; establish operating rhythms and executive-ready status reporting.

• Define and enforce standards for data products, semantic models, KPI definitions, model documentation, and lifecycle management.

• Ensure analytics and AI solutions comply with enterprise security, privacy, data retention, and regulatory requirements; partner with InfoSec and Risk on controls.

• Establish responsible AI practices (use-case review, human-in-the-loop where needed, monitoring for drift/bias, and incident response) and drive audit-ready evidence.

• Identify and lead opportunities to modernize the analytics and AI platform (cloud data stack, self-service BI, feature/model stores, and GenAI tooling).

• Implement scalable delivery and operations (CI/CD, automated testing, observability, cost management) to improve reliability, speed, and reuse across analytics and AI products.

• Bring a strong understanding of the manufacturing domain, preferably windows manufacturing, to align data models and analytics solutions with plant, product, and order-management processes.

• Apply advanced dimensional modeling (Kimball, Inmon, Data Vault) to design star schemas, snowflake models, fact/dimension tables, and enterprise data models.

• Lead end-to-end ETL/ELT pipeline development, including ingestion, transformation, reconciliation, and loading pipelines using tools such as SSIS, ODI, and Matillion.

• Drive data governance, data quality, and Master Data Management (MDM), including trust rules, match/merge logic, lineage analysis, DQ dashboards, reconciliation processes, and MDM rule sets.

• Apply strong SQL engineering skills, writing complex SQL, PL/SQL, stored procedures, materialized views, and CTEs, with a focus on performance tuning.

• Maintain data lineage, metadata management, and documentation, including data dictionaries, mapping documents, lineage tracking, and metadata-driven data warehouse frameworks.

• Provide leadership on data warehouse projects and cross-functional collaboration,…