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.

• Data Strategy & Architecture
  
  
  
  • Define and own theenterprise data strategy in alignment with corporate goals.
  • Design and maintaincomprehensive data architecture spanning ERP, HRIS, CRM, ATS, Finance, and other core business systems.
  • Ensure the architecture supportsscalability, 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 accountability-defining 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 aUniversal Data Model and ensure consistent definitions across all systems.
  • Steer thedesign and implementation of integrations between business systems using APIs, middleware, and event-driven architectures.
  • Coordinatedata orchestration pipelines to unify, cleanse, and harmonize data from multiple sources.
  
  
  
  
• Innovation & Continuous Improvement
  
  
  
  • Evaluate emerging data technologies, tools, and best practices to recommendinnovative solutions that drive efficiency and insight.
  • Continuously improve data quality, monitoring frameworks, and performance metrics.
  • Lead efforts todemocratize data access, empowering self-service analytics across the enterprise.
  
  
  
  

• 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

• 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 indata modeling, MDM, Meta Data, and data governance frameworks.
  • Experience withcloud-based data platforms (AWS, Azure, GCP) and modern data stacks (e.g., Redshift, BigQuery, Snowflake).
  • Experience withintegration platforms (ETL/ELT, APIs, event streaming, middleware).
  • Knowledge ofdata security (tokenization, encryption, access controls) and compliance frameworks.
  
  
  
  
• Leadership & Soft Skills
  
  
  
  • Strong ability toinfluence 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 balancelong-term architecture vision with short-term delivery needs.