Enterprise Data Architect

Enterprise Data Architect (EDA) The Enterprise Data Architect (EDA) is responsible for defining and advancing the company's enterprise-wide data strategy across all non-product engineering business functions. In this role, the EDA will lead the development of a comprehensive framework to design, implement, and govern enterprise data. This effort will integrate diverse business systems, unify fragmented data, and provide accurate, trusted, and easily accessible information to empower decision-making and drive operational excellence. The EDA will define and champion the enterprise-wide data governance framework, aligning business and technology needs to deliver seamless processes for functional leaders and empower technical teams to create scalable, future-ready solutions. This role is especially critical in a fast-growing organization, where data consistency and reliability are foundational to operational excellence and future scalability. Key Responsibilities Data Strategy & Architecture Define and own the enterprise data strategy in alignment with corporate goals. Design and maintain comprehensive data architecture spanning ERP, HRIS, CRM, ATS, Finance, and other core business systems. Ensure the architecture supports scalability, performance, and resilience as the company grows. Lead initiatives to break down data silos and establish trusted, unified sources of truth across the organization this may include building a enterprise-wide data warehouse. Data Governance & Stewardship Champion a company-wide data governance framework that fosters a culture of data accountabilitydefining clear policies, ownership, stewardship, quality standards, and lifecycle management to ensure data is trusted and actionable. Facilitate Regular Meetings: Schedule recurring governance council sessions with functional leaders, data stewards, and technical specialists to review priorities, align on decisions, and resolve data issues. Drive Cross-Functional Collaboration: Act as the bridge between business and technology teams to ensure shared accountability for data quality and consistency. Identify & Track Data Gaps: Continuously monitor for data inconsistencies, missing attributes, or system misalignments across source systems. Coordinate Remediation Efforts: Work with system owners and data stewards to resolve data gaps and improve data accuracy. Implement Monitoring & Alerts: Establish automated data quality checks and exception reporting to proactively detect and address issues. Maintain Governance Policies: Keep data ownership, stewardship, and lifecycle management policies up to date. Ensure Compliance: Monitor adherence to data standards and escalate when policies are not followed. Measure Effectiveness: Track key data governance KPIs such as data quality scores, reconciliation error rates, and time-to-resolution for data issues. Continuously Improve: Refine governance processes, adjust frameworks, and adopt new tools or methodologies to enhance efficiency and impact. Data Integration & Unification Champion the adoption of a Universal Data Model and ensure consistent definitions across all systems. Steer the design and implementation of integrations between business systems using APIs, middleware, and event-driven architectures. Coordinate data orchestration pipelines to unify, cleanse, and harmonize data from multiple sources. Innovation & Continuous Improvement Evaluate emerging data technologies, tools, and best practices to recommend innovative solutions that drive efficiency and insight. Continuously improve data quality, monitoring frameworks, and performance metrics. Lead efforts to democratize data access, empowering self-service analytics across the enterprise. Reporting Structure Reports To: Chief Financial Officer (CFO). Direct Reports: May lead data engineers, data modelers, data analysts or solution architects as the data function matures. Primary Stakeholder Groups: Sales operations Marketing operations IT & Security Finance, Legal, Operations and People functions Business analytics specialists across the business Qualifications Education & Experience Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field. 10+ years of experience in data architecture, data engineering, or enterprise systems integration. Proven track record in designing enterprise-level data architectures in complex, multi-system environments. Technical Skills Expertise in data modeling, MDM, Meta Data, and data governance frameworks. Experience with cloud-based data platforms (AWS, Azure, GCP) and modern data stacks (e.g., Redshift, BigQuery, Snowflake). Experience with integration platforms (ETL/ELT, APIs, event streaming, middleware). Knowledge of data security (tokenization, encryption, access controls) and compliance frameworks. Leadership & Soft Skills Strong ability to influence cross-functional stakeholders and drive consensus across departments. Excellent communication skills, capable of presenting complex data concepts to both technical and non-technical audiences. Strategic thinker with the ability to balance long-term architecture vision with short-term delivery needs. Estimated Annual Pay Range $200,000 - $275,000 USD Verkada Is An Equal Opportunity Employer As an equal opportunity employer, Verkada is committed to providing employment opportunities to all individuals. All applicants for positions at Verkada will be treated without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law.