Technical Architect - Enterprise Data

Arista Networks•Dallas, TX

About The Position

The Technical Architect - Enterprise Data defines, governs, and delivers scalable enterprise data and integration architectures across Microsoft Fabric, Azure, and core business systems. The role provides architectural leadership, ensures alignment with enterprise standards, and guides engineering teams in implementing secure, high-quality, and performant solutions. It partners with business stakeholders, data teams, systems and security teams, and application owners to translate requirements into technical and functional blueprints and long-term platform roadmaps. The position reports to the IT Applications team and works with key IT stakeholders. It can be located anywhere in the United States. The architect establishes enterprise data architecture and integration patterns, sets standards for modeling and governance, and supports platform reliability, quality, security, cost optimization, and operational excellence. The role also integrates AI/ML and GenAI capabilities into data products, evaluates emerging technologies, and guides teams in responsible AI practices. The architect defines continuity strategies across data and integration platforms, including ERP, CRM, Data Lake, Fabric, and analytics systems. This includes proactively identifying architectural risks, ensuring disaster recovery and continuity considerations are designed into solutions, enabling teams to execute documented recovery plans, and ensuring plans meet audit and security requirements. The position sets expectations for vendor-led work, establishes quality gates and acceptance criteria, monitors vendor progress and risks, and provides technical guidance to vendor engineers to ensure solutions follow enterprise architecture and coding standards.

Requirements

10+ years in enterprise solution architecture, data engineering, and software development.
Proven experience designing end-to-end integrations, data platforms, and automation for large enterprise environments.
Strong hands-on expertise with Microsoft Fabric, Data Lake, Data Warehouse, Data Pipelines, and the broader Microsoft ecosystem.
Deep experience with Power BI semantic models, datasets, dashboards, and reporting.
Advanced proficiency in DAX, Power Query, SQL, Python, and PySpark.
Demonstrated experience architecting ETL/ELT pipelines that integrate diverse data sources into Azure Data Lake and EDW.
Strong understanding of data modeling, normalization, metadata management, and enterprise data security.
Excellent communication, stakeholder management, and problem-solving skills.

Nice To Haves

A degree preferably in Computer Science, Information Technology, Management Information Systems, or Accounting.
Exposure to AI/ML, Copilot, GenAI, LLMs, prompt engineering, and AI API integration.
Familiarity with SDLC, software quality practices, Jira, Git, and CI/CD.
Experience with SaaS/cloud ERP or CRM systems such as NetSuite, Salesforce, or SAP S/4HANA.

Responsibilities

Define enterprise data architecture across Fabric, Data Lake, EDW, and analytics platforms.
Design integration patterns for data ingestion, transformation, and consumption.
Create architectural flows and reference models.
Establish standards for data modeling, metadata, lineage, and governance.
Implement data validation, logging, monitoring, lineage, and role-based access control (RBAC).
Ensure compliance with enterprise security, confidentiality, and regulatory standards.
Drive platform cost optimization, performance tuning, and operational excellence.
Enforce architectural governance and adherence to standards.
Collaborate with business stakeholders and source system teams to understand data requirements.
Translate business needs into technical specifications and architectural roadmaps.
Review user stories and provide architectural guidance to development teams.
Support QA and functional teams in defining testing strategies for new and existing features.
Ensure delivered solutions align with business needs and architectural intent.
Integrate AI/ML models and GenAI capabilities into data products and workflows.
Evaluate emerging technologies and recommend adoption strategies.
Guide teams on responsible AI usage and integration patterns.
Conduct root cause analysis for data issues and identify opportunities for improvement.
Manage platform reliability, data quality, and governance processes.
Define continuity strategies for data and integration platforms.
Identify architectural risks, including single points of failure, dependency bottlenecks, and data corruption risks.
Ensure solutions include disaster recovery and continuity considerations from the outset.
Design continuity across ERP, CRM, Data Lake, Fabric, and analytics systems so business processes can function during partial outages.
Ensure teams understand system recovery procedures, documentation locations, and how to execute continuity plans.
Ensure continuity plans meet audit and security requirements.
Define expectations for vendor-led work, including process adherence, development methodology, documentation, and delivery timelines.
Establish quality gates, review cycles, and acceptance criteria for vendor deliverables.
Conduct checkpoints with vendor teams to monitor progress, identify risks, and ensure alignment with enterprise architecture principles.
Provide vendor engineers with technical guidance to ensure solutions are scalable, secure, and compliant with enterprise patterns.