Technical Architect - Enterprise Data

The Technical Architect - Enterprise Data is responsible for defining, governing, and delivering scalable enterprise data and integration architectures across Microsoft Fabric, Azure, and core business systems. This role provides architectural leadership, ensures alignment with enterprise standards, and guides engineering teams in implementing highquality, secure, and performant solutions. The architect partners with business stakeholders, data teams, systems and security team and application owners to translate requirements into technical & functional blueprints and longterm platform roadmaps. The role will report to the IT Applications team and will work with key IT stakeholders.

What You’ll Do

Role Responsibilities:

• Define Enterprise Data Architecture across Fabric, Data Lake, EDW, and analytics platforms.
• Design Integration Patterns for 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 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.
• The architect defines the continuity strategy for all data and integration platforms, not just implements it.
• Proactively identifies architectural risks (single points of failure, dependency bottlenecks, and data corruption risks).
• Ensures every solution includes DR, and continuity considerations from day one-not bolted on later.
• Designs continuity across ERP, CRM, Data Lake, Fabric, and analytics systems, ensuring business processes remain functional even during partial outages.
• Ensures teams know how to recover systems, where documentation lives, and how to execute continuity plans.
• Ensures continuity plans meet audit, and security requirements.

Set Vendor Expectations

• Define clear standards for process adherence, development methodology, documentation, and delivery timelines for all vendor led work.

Drive Quality Accountability

• Establish quality gates, review cycles, and acceptance criteria to ensure vendor deliverables meet architectural and coding standards.
• Conduct regular checkpoints with vendor teams to monitor progress, identify risks, and ensure alignment with enterprise architecture principles.
• Provide technical guidance to vendor engineers to ensure solutions are scalable, secure, and compliant with enterprise patterns.

• Degree preferably in Computer Science, Information Technology, Management Information Systems, or Accounting.
• Proven experience designing endtoend 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 integrating diverse data sources into Azure Data Lake and EDW
• Strong understanding of data modeling, normalization, metadata management, and enterprise data security.
• Exposure to AI/ML, Copilot, GenAI, LLMs, prompt engineering, and AI API integration.
• Familiarity with SDLC, software quality practices, Jira, Git, CI/CD.
• Experience with SaaS/Cloud ERP or CRM systems (NetSuite, Salesforce, SAP S/4HANA)
• Excellent communication, stakeholder management, and problem solving skills