IT Data Science Principal (Data Warehouse)

Cornerstone Building Brands•Cary, NC

3d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in Information Technology, Data Analytics, Business, or a related field.
8 plus 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 for semantic-layer-driven natural language analytics, warehouse cost/performance tuning, and role-based access governance.
Experience designing and operating a medallion architecture (Bronze/Silver/Gold), with clear separation between raw replication, single-source-of-truth business definitions, and consumer-specific customizations.
Experience consolidating data from multiple heterogeneous ERP and legacy source systems (e.g., JDE, EBS, SAP based systems, and similar) into a governed enterprise platform, including reconciliation across inconsistent business definitions and buying-group/customer hierarchies.
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 environments, including MLOps practices and cross-functional delivery.
Exposure to responsible AI/model risk management, enterprise data governance, and Agile/DevOps delivery methodologies.
Demonstrates practical AI literacy and the ability to thoughtfully use AI‑enabled tools to enhance productivity, decision‑making, and quality of work.

Nice To Haves

Understanding of the manufacturing domain, preferably windows manufacturing.

Responsibilities

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, leading data engineering teams, managing stakeholders, gathering requirements, and delivering enterprise data warehouse solutions across multiple business units.
Bring cloud data warehousing exposure (Snowflake), including hands-on experience modernizing legacy warehouses, implementing Medallion architecture, and managing cloud-native data platforms.