Chris Paskins Email & Phone Number

Phoenix, Az, Us

Irving, Texas, Us

Rancho Cordova, California, Us

Established a data, analytics, and governance team, enabling data-driven decision-making across the enterprise.- Stood up a business unit encompassing Data Strategy, Data Science, Business Intelligence, Governance and Quality with a budget of $8.5M and 35 direct reports. - Guided executive leadership on data strategies and execution, owning detailed roadmaps, schedules, and budgets that align with strategic objectives and drive the implementation of data products.- Achieved $12M in cost savings by spearheading the deployment of an Azure analytics platform, utilizing Terraform, ADF, dbt, Snowflake, and PowerBI enabling self-service analytics capabilities within one year.- Led the data engineering team to implement all ETL/ELT pipelines and integrations, data modeling, data storage, access & security management, and CI/CD practices for the analytics and reporting platform. Data sources include SAP, Model-N, MS Dynamics, Windchill, Salesforce, Workday.- Captured $25M in cost savings by harnessing machine learning models (ensemble, auto-encoders, neural networks), for large-scale data migration validation, anomaly detection, and demand forecasting.- Reduced GDPR and CCPA risk exposure 25% by creating and implementing a data governance operating model with structured bodies, policies, standards, and accountability matrices.- Created a data stewardship community with over 150 members and led the Enterprise Data Governance Council facilitating key data architectural and privacy design approvals.- Increased data quality conformance to 98.5% by implementing comprehensive data quality and observability measures across all ingestion pipelines, utilizing tools such as Python, dbt tests, supervised and unsupervised machine learning models and products.- Implemented DataHub in Azure Kubernetes Service (AKS), to centralize metadata storage and streamline data lineage tracking, enhancing data discoverability, compliance, and ensured robust data governance.

San Jose, Ca, Us

Directed a corporate data transformation initiative that fortified data analytics, governance, and quality capabilities across all business units, encompassing 70,000+ employees globally.- As a member of the advanced analytics leadership steering committee, played a key role in driving down operational costs by $350M over three years.- Led the data migration, integration, and rationalization for three multi-billion-dollar companies, unifying disjointed Master Data Management and Governance technology and processes into a cohesive platform utilizing Oracle’s Data Relationship Management, Customer Hub, and Product Data Management.- Directed the data science team to develop supervised and unsupervised machine learning models and products to analyze over 1.5 billion records daily, yielding an 18% increase in data quality conformance.- Developed and implemented a comprehensive enterprise data governance operating model, ensuring adherence to GDPR and CCPA focusing on data accuracy, and privacy, slashing enterprise risk by 10%.- Documented architectural designs, data flows, and procedures for the AWS analytics platform, establishing and upholding best practices for data management and governance.- Implemented Informatica Data Catalog for the AWS analytics platform, enhancing data discovery, governance, and management, resulting in improved data quality, and streamlined data asset utilization.- Scaled the data stewardship community to over 550 members and led the Enterprise Data Governance Council to oversee key data management, architectural, and privacy approvals.

San Jose, Ca, Us

Developed a corporate data quality program to ensure accurate, consistent, and reliable data, driving informed decision-making, operational efficiency, productivity, and business performance.- Created leading-edge data quality/observability platform utilizing machine learning in AWS utilizing Python, Snaplogic, Redshift, Databricks, PostgreSQL, and Tableau to measure, store, and visualize data quality KPI’s with enterprise-wide transparency.- Reduced operational costs by $35M by leading high-impact data quality initiatives that focused on new analytics capabilities targeted to reduce capex spend and operational efficiency.- Increased data quality conformance to 99.2% following creation and implementation of enterprise-level data quality strategy, framework, maturity model and project playbook for continuous improvement.- Evangelized data quality and literacy by creating a Data Quality Center of Excellence (CoE), fostering best practices and continuous improvement in data governance across the organization.

San Jose, Ca, Us

Spearheaded the creation of a robust analytics platform that transformed data utilization and decision-making processes, enabling advanced analytics adoption across the enterprise.- Established a Data Science Center of Excellence promoting analytics literacy, skills, and processes to enable cross-functional teams to start and scale data science initiatives.- Saved $3M annually by utilizing supervised and unsupervised machine learning algorithms for identifying root causes for product quality / reliability issues and forecasting electrical performance.- Achieved a 400% increase in efficiency for large volume queries by engineering and implementing a columnar data architecture utilizing Hadoop, Hive, MapReduce, SQL Server Integration Services, T-SQL, MS SQL Server, SAS and Spotfire outperforming legacy data solutions.- Improved reporting efficiency by 75% by standardizing historical reports on product performance and employing data visualization with alerts and trends to remove blockers for issue resolution.

Geneva, Switzerland, Ch

Oversaw all aspects of wafer fabrication, including profit, testing, and quality control of technologies. Created an analytics program and platform, applying early advanced analytical techniques where manpower was lacking. The goal was to find new ways to identify and solve issues faster to keep wafer and labor costs low.- Identified processing and/or integration-related issues and implemented manufacturing solutions to increase wafer fab yields, decrease defect density, boost electrical sort yield, and increase overall revenue per wafer for BCD, Bi-CMOS, and CMOS technologies. - Engineered a comprehensive analytics data warehouse to fulfill the unmet need for advanced analytics thus delivering a robust analytical solution servicing the organization.- Increased electrical yields 12%, by engineering analytical models utilizing regression, decision trees, and neural networks to predict yield and recommend strategic product enhancements.

Certificate, Applied Data Science

Master Of Science - Ms, Management Information Systems, General

Bachelor Of Arts - Ba, Mathematics

Certificate, High Impact Project Management