Data Architect Clorox is the place that's committed to growth – for our people and our brands. Guided by our purpose and values, and with people at the center of everything we do, we believe every one of us can make a positive impact on consumers, communities, and teammates. Join our team. Your role at Clorox: We are seeking an experienced and hands-on Data Architect to join the Data Architecture & Strategy team and serve as a key technical leader in defining and advancing the enterprise data architecture vision. Reporting directly to the Associate Director of Data Architecture & Strategy, this role will be responsible for designing scalable and governed data ecosystems, establishing architectural standards, enabling modern analytics and AI capabilities, and partnering with business and technology teams to deliver enterprise-grade data solutions. The ideal candidate combines deep expertise in modern cloud data platforms, data architecture, data modeling, and analytics with strong business acumen and the ability to communicate complex technical concepts to both technical and non-technical audiences. This individual will work closely with business units, platform vendors, engineering teams, and managed service providers to design, implement, operationalize, and continuously improve enterprise data platforms and data products.
In this role, you will
Enterprise Data Architecture:
Define and evolve enterprise data architecture standards, reference architectures, and solution patterns.
Design scalable, secure, and governed data architectures supporting analytics, reporting, AI, and operational use cases.
Establish architectural guardrails and best practices across data platforms, data products, and analytics solutions.
Drive enterprise data modernization initiatives and recommend emerging technologies, patterns, and capabilities.
Data Platform Architecture:
Design and govern cloud-based data platforms built on Azure technologies.
Lead architecture efforts involving Microsoft Fabric, Azure Data Services, Databricks, Power BI, and related technologies.
Define architecture patterns for ingestion, transformation, storage, semantic modeling, and consumption.
Develop strategies to improve platform scalability, performance, reliability, security, and cost efficiency.
Data Modeling & Semantic LayerDesign conceptual, logical, and physical data models for enterprise and domain-specific data solutions.
Define common enterprise data models and reusable business entities.
Establish and govern enterprise semantic layers to support trusted and consistent analytics across business functions.
Promote data products and domain-oriented architecture principles to improve data discoverability and reuse.
AI-Ready Data Foundations:
Develop architecture strategies that enable AI, machine learning, and advanced analytics initiatives.
Ensure enterprise data assets are governed, documented, discoverable, and suitable for AI consumption.
Collaborate with data engineering, analytics, and AI teams to establish scalable data foundations supporting future AI innovation.
Architecture Consultation & Business Engagement:
Partner with business units, analytics teams, and product teams to understand business requirements and translate them into architecture solutions.
Provide architecture reviews and technical consultation for self-service analytics, reporting, and data product initiatives.
Communicate technical risks, tradeoffs, and recommendations in clear business-friendly language.
Build strong relationships with enterprise and functional leaders to drive alignment on strategic initiatives.
Platform Vendor & Operational Partnership:
Collaborate with platform vendors on architecture design, solution roadmaps, and implementation planning.
Participate in solution design workshops and deployment activities.
Work closely with managed service providers and operational teams to ensure solutions are supportable and operationally sustainable.
Define operational processes, monitoring approaches, support models, and handoff procedures.
Create architecture documentation, runbooks, and operational guidance for long-term platform management.
Engineering Excellence & Automation:
Remain hands-on with architecture validation, prototyping, and technical implementation activities.
Guide CI/CD practices for data engineering and analytics workloads.
Promote Infrastructure as Code, Architecture as Code, Spec-Driven Development, and automation-first approaches.
Support engineering teams in implementing architecture standards and modern development practices.
What we look for:- 8+ years of experience in Data Architecture, Data Engineering, Analytics Architecture, or related disciplines.
- Proven success designing and implementing enterprise-scale cloud data platforms and solutions.
- Deep expertise in enterprise data architecture, data products, data mesh, and domain-driven design principles.
- Strong knowledge of data governance, metadata management, data quality, master data, and data security architectures.
- Hands-on experience with Microsoft Azure, Microsoft Fabric, Databricks, Delta Lake, Azure Data Factory, ADLS, and Power BI. (Experience with other platforms like AWS and GCP would be a plus)
- Experience developing conceptual, logical, and physical data models, including dimensional, enterprise, canonical, and semantic models.
- Strong understanding of metadata, lineage, and semantic layer design strategies.
- Advanced proficiency in Python, SQL, automation, and modern data engineering practices.
- Experience with DevOps, CI/CD pipelines, Git-based development, and software engineering best practices.
- Knowledge of Infrastructure as Code (IaC), Architecture as Code, Policy as Code, platform engineering, and related automation frameworks.
- Demonstrated ability to influence executive stakeholders and communicate complex technical concepts to business audiences.
- Proven track record leading enterprise-wide architecture transformation initiatives while balancing strategic vision with hands-on execution.
- Experience partnering with business leaders, software vendors, consulting organizations, and managed service providers to deliver scalable solutions.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
Preferred:
Experience building AI-ready data platforms, self-service analytics ecosystems, and enterprise semantic layers; cloud certifications (Azure, Databricks, Fabric) and experience in consumer goods, manufacturing, supply chain, or other large-scale enterprise environments.
