Principal AI Data Engineer

Presidio, Where Teamwork and Innovation Shape the Future

At Presidio, we're at the forefront of a global technology revolution, transforming industries through cutting-edge digital solutions and next-generation AI. We empower businesses - and their internal customers - to achieve more through innovation, automation, and intelligent insights.

The Role

Responsibilities Include:

Technical Leadership

• Establish engineering standards, development practices, and implementation patterns for enterprise AI and data platform solutions.
• Mentor engineers through architecture reviews, code reviews, technical coaching, and engineering best practices.
• Evaluate emerging technologies and recommend improvements to the enterprise AI and data platform.
• Partner with the AI Data Architect to translate enterprise strategy into scalable, secure, and production-ready technical solutions.
• Promote engineering excellence across reliability, maintainability, automation, and operational support.
• Provide technical leadership in evaluating implementation trade-offs and recommend improvements that strengthen the enterprise architecture while maintaining alignment with strategic objectives.

Data Platform Engineering (Microsoft Fabric & Azure)

• Build and operate the enterprise lakehouse on Microsoft Fabric and Microsoft Azure, implementing the domain-oriented data products, medallion-layer structures, and Fabric-based semantic models defined in the enterprise architecture.
• Develop, test, and maintain data pipelines for ingestion, transformation, and serving using Fabric-native tooling, Python, Spark, and SQL, with automated data validation to ensure integrity and timeliness.
• Administer the Fabric and Azure data environments: capacity, workspaces, deployment pipelines, monitoring, and cost management.
• Own performance tuning and operational excellence for the data platform, including incident response, root-cause analysis, and continuous improvement.
• Establish and maintain engineering practices for the platform: version control, CI/CD, code review, testing standards, and release management.

Semantic Model & Data Product Implementation

• Implement enterprise semantic models and certified data products to specification, encoding governed metric definitions, calculation logic, and business context from the metrics registry.
• Implement row-level and object-level security in Fabric and OneLake that mirrors source-system permissions (e.g., Salesforce roles and visibility rules) to protect sensitive pipeline, customer, and people data.
• Integrate source systems — CRM (Salesforce), CPQ, PSA, ERP, HRIS, and finance platforms — into the enterprise model so revenue, pipeline, people, cost, and customer data are consistently defined and analytics-ready.
• Modernize data flows from legacy and server-based applications into the lakehouse, with reconciliation and validation frameworks that prove parity between legacy outputs and modernized models.
• Connect governed, certified data sources to Data Visualization Platforms (e.g., Power BI, Tableau) and partner with BI developers to migrate duplicated logic into shared enterprise models.

AI Solution Engineering

• Build the retrieval and grounding infrastructure — semantic model endpoints, metadata services, RAG patterns, certified MCP connectors, and context APIs — that lets AI applications and agents answer business questions with governed data.
• Engineer the enterprise context layer in partnership with the AI Data Architect and AI Enablement function, making curated business context, policies, and definitions available to AI tools.
• Implement guardrails, access controls, and quality gates for AI data consumption in accordance with company policies.

Data Quality & Operations

• Implement automated data quality frameworks: validation rules, anomaly detection, reconciliation checks, and monitoring aligned to established quality standards.
• Maintain lineage, documentation, and metadata for pipelines, models, and data products to support governance, certification, and auditability.
• Support current-state assessment and knowledge capture from existing systems, prior development efforts, and third-party contractors, converting institutional knowledge into documented, maintainable code.

Collaboration

• Partner daily with the AI Data Architect to refine designs based on implementation realities, propose technical alternatives, and deliver iteratively.
• Work with BI developers, analysts,