IT Data Science Principal, Data Warehouse
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Job Description
Leading enterprise data warehouse, analytics, and AI capabilities for Cornerstone Building Brands, a North American building-products manufacturer. Driving roadmaps, governance, modernization, and multidisciplinary delivery.
Responsibilities:
- Develop enterprise analytics and AI roadmaps aligned with business strategy, value, and feasibility
- Establish prioritization, intake, and funding recommendations for analytics, ML, and GenAI initiatives
- Define success metrics and track benefits for analytics and AI products and services
- Lead, coach, and partner with multidisciplinary analytics, BI, data science/ML, and vendor teams
- Build a high-performing organization focused on customer value, operational excellence, and continuous learning
- Partner with business and functional leaders to identify, shape, and deliver analytics and AI use cases
- Translate business needs into product backlogs and release plans
- Advise on KPI design, experimentation, model performance, adoption, change management, and training
- Communicate priorities, timelines, risks, and decisions; establish executive-ready status reporting
- Define and enforce standards for data products, semantic models, KPI definitions, model documentation, and lifecycle management
- Ensure compliance with security, privacy, data retention, and regulatory requirements
- Establish responsible AI practices, including use-case review, human-in-the-loop controls, drift/bias monitoring, incident response, and audit-ready evidence
- Modernize analytics and AI platforms, including cloud data stacks, self-service BI, feature/model stores, and GenAI tooling
- Implement scalable delivery and operations such as CI/CD, automated testing, observability, cost management, and reuse
- Apply manufacturing-domain knowledge to align data models and analytics with plant, product, and order-management processes
- Design dimensional models, 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
- Drive data governance, data quality, and Master Data Management, including trust rules, match/merge logic, lineage, DQ dashboards, reconciliation, and MDM rule sets
- Maintain data lineage, metadata, documentation, data dictionaries, mapping documents, and metadata-driven warehouse frameworks
- Lead data warehouse projects and cross-functional collaboration across multiple business units
- Modernize legacy warehouses and manage cloud-native data platforms
Requirements:
- Bachelor’s degree in Information Technology, Data Analytics, Business, or a related field
- 8+ years of experience across enterprise analytics, business intelligence, and/or data science/ML delivery in a medium-to-large enterprise environment
- Proven experience leading teams and portfolios delivering analytics products and AI solutions, including vendor management, roadmap execution, and stakeholder governance
- Hands-on experience with Snowflake as an enterprise platform, including Cortex Analyst / Snowflake Intelligence, warehouse cost/performance tuning, and role-based access governance
- Experience designing and operating a medallion architecture (Bronze/Silver/Gold)
- Experience consolidating data from heterogeneous ERP and legacy source systems such as JDE, EBS, and SAP-based systems
- Strong people leadership, coaching, and stakeholder management skills
- Ability to translate business strategy into analytics and reporting solutions
- Ability to manage multiple priorities in a fast-paced, matrixed environment
- Experience delivering AI/ML or GenAI solutions in production, including MLOps practices and cross-functional delivery
- Exposure to responsible AI/model risk management, enterprise data governance, and Agile/DevOps methodologies
- Practical AI literacy and ability to use AI-enabled tools to enhance productivity, decision-making, and quality of work
- Strong understanding of the manufacturing domain, preferably windows manufacturing
- Advanced dimensional modeling using Kimball, Inmon, or Data Vault
- Experience with ETL/ELT tools such as SSIS, ODI, and Matillion
- Strong SQL engineering skills, including complex SQL, PL/SQL, stored procedures, materialized views, CTEs, and performance tuning
- Experience with data lineage, metadata management, documentation, data dictionaries, mapping documents, and metadata-driven data warehouse frameworks
- Cloud data warehousing exposure, particularly Snowflake, including legacy warehouse modernization and cloud-native data platforms
Benefits:
- Bonus
- Medical, dental, and vision benefits starting day 1
- PTO
- Paid holidays
- Flexible Spending Account (FSA)
- Life insurance
- Long-term disability (LTD)
- Short-term disability (STD)
- 401(k)
- Employee Assistance Program (EAP)
- Discount programs
- Tuition reimbursement
- Training
- Professional development
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