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Cornerstone Building Brands

Manufactures exterior building solutions for buildings

IT Data Science Principal - Data Warehouse

Data Management +1Updated on Nov 13
Full-timeCary, NC, USARemote

About the Job

Requirements

  • A bachelor's degree in Information Technology, Data Analytics, Business, or a related field is required.
  • Snowflake SnowPro Certification is required.
  • At least 8 years of experience across enterprise analytics, business intelligence, and/or data science or machine learning delivery in a medium-to-large enterprise environment is required.
  • Proven experience leading teams and portfolios delivering analytics products and AI solutions, including vendor management, roadmap execution, and stakeholder governance is required.
  • Hands-on experience with Snowflake as an enterprise platform, including Cortex Analyst or Snowflake Intelligence for semantic-layer-driven natural language analytics, warehouse cost and performance tuning, and role-based access governance is required.
  • Experience designing and operating a medallion architecture (Bronze/Silver/Gold), with separation between raw replication, single-source-of-truth business definitions, and consumer-specific customizations is required.
  • Experience consolidating data from multiple heterogeneous ERP and legacy source systems, such as JDE, EBS, and SAP-based systems, into a governed enterprise platform is required, including reconciliation across inconsistent business definitions and buying-group/customer hierarchies.
  • Strong people leadership, coaching, and stakeholder management skills are required.
  • The ability to translate business strategy into analytics and reporting solutions is required.
  • The ability to manage multiple priorities in a fast-paced, matrixed environment is required.
  • Experience delivering AI, machine learning, or generative AI solutions in production environments, including MLOps practices and cross-functional delivery, is required.
  • Exposure to responsible AI and model risk management, enterprise data governance, and Agile/DevOps delivery methodologies is required.
  • Practical AI literacy and the ability to use AI-enabled tools thoughtfully to enhance productivity, decision-making, and work quality are required.
  • A strong understanding of the manufacturing domain is required; experience in window manufacturing is preferred.
  • Experience with dimensional modeling methods including Kimball, Inmon, and Data Vault is required.
  • Experience with data warehouse platforms and technologies including SSIS, ODI, Matillion, SQL, PL/SQL, stored procedures, materialized views, and common table expressions is required.
  • Experience with data governance, data quality, master data management, data lineage, metadata management, cloud data warehousing, Snowflake, and Medallion architecture is required.

Responsibilities

  • Develop the enterprise analytics and AI roadmap, aligning use cases and investments with business strategy, value, and feasibility.
  • Establish outcome-based prioritization, intake, and funding recommendations for analytics, machine learning, and generative AI initiatives.
  • Define success metrics and benefits tracking for analytics and AI products and services, including adoption, quality, cycle time, and business impact.
  • Lead, coach, and partner with multidisciplinary teams, including analytics engineers, BI developers and analysts, data scientists, machine learning engineers, and vendor or partner resources.
  • Set expectations for delivery quality, documentation, testing, and responsible AI practices, and 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, translating needs into a product backlog and release plan.
  • Advise on KPI design, experimentation, model performance, and adoption considerations, including process, change management, and training.
  • Communicate priorities, timelines, risks, and decisions, and 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, partnering with Information Security and Risk on controls.
  • Establish responsible AI practices, including use-case review, human-in-the-loop processes where needed, monitoring for drift and bias, incident response, and audit-ready evidence.
  • Identify and lead opportunities to modernize the analytics and AI platform, including the cloud data stack, self-service business intelligence, feature and model stores, and generative AI tooling.
  • Implement scalable delivery and operations practices, including continuous integration and continuous delivery, automated testing, observability, and cost management, to improve reliability, speed, and reuse.
  • Align data models and analytics solutions with plant, product, and order-management processes in the manufacturing domain.
  • Design star schemas, snowflake models, fact and dimension tables, and enterprise data models using dimensional modeling practices.
  • Lead end-to-end extract, transform, and load or extract, load, and transform pipeline development, including ingestion, transformation, reconciliation, and loading.
  • Drive data governance, data quality, and master data management, including trust rules, match/merge logic, lineage analysis, data-quality dashboards, reconciliation processes, and master data management rule sets.
  • Write complex SQL, PL/SQL, stored procedures, materialized views, and common table expressions, focusing on performance tuning.
  • Maintain data lineage, metadata, 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, leading data engineering teams, managing stakeholders, gathering requirements, and delivering enterprise data warehouse solutions across multiple business units.
  • Modernize legacy data warehouses, implement Medallion architecture, and manage cloud-native data platforms including Snowflake.

Desired Qualifications

  • Experience in window manufacturing.

About the Company

Cornerstone Building Brands

Manufactures exterior building solutions for buildings

Cornerstone Building Brands produces exterior building solutions for homes and commercial properties across North America. Its offerings include metal buildings, cold-formed buildings, and rigid frame buildings, designed to be durable, affordable, and quick to assemble for a variety of low-complexity projects. The company manufactures and sells these exterior products and sells directly to contractors, builders, and other industry professionals, focusing on strong partnerships to deliver value and performance. The main difference from competitors lies in its broad product family and direct customer-focused approach, aiming to provide reliable, long-lasting solutions while engaging with communities and stakeholders. The goal is to create value for customers and contribute positively to the communities where people live and work, by helping customers build a better tomorrow.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Cary, NC
Founded
1984

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