Principal AI Data Engineer

Presidio•Austin, TX

About The Position

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, and domain teams to gather technical requirements and deliver reliable, well-documented data products. Mentor and review the work of internal engineers and contractors, raising the engineering bar across the data function.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent practical experience.
10+ years progressive experience in data engineering, software engineering, cloud data platforms, or enterprise analytics engineering.
5+ years of hands-on experience designing, building, and operating enterprise data platforms using Microsoft Azure, Microsoft Fabric, Databricks, Snowflake, or comparable data technologies.
Significant hands-on experience building and operating enterprise data platforms in production, including lakehouse and medallion architectures, domain-oriented data products, and semantic models.
Deep, hands-on expertise with Microsoft Fabric and Microsoft Azure data services: lakehouse, Data Factory, notebooks, semantic models, deployment pipelines, and identity-based access control (e.g., Entra ID).
Proven experience consolidating heterogeneous legacy source systems (e.g., mainframe, Oracle, PostgreSQL, on-premises SQL Server) into modern cloud data platforms, including reconciliation and validation across migrations.
Strong programming skills in Python/PySpark and SQL, with experience engineering high-volume production pipelines with automated auditing, validation, and recovery patterns.
Hands-on Salesforce experience, including administration and integration of SFDC data and permission models into analytical platforms.
Experience implementing row-level security and access controls in analytics platforms that mirror source-system permission models.
Demonstrated engineering discipline: version control, structured deployment (e.g., Fabric deployment pipelines), testing, and production support.
Strong communication skills and the ability to work effectively with architects, analysts, business stakeholders, and third-party contractors.

Nice To Haves

Experience building AI-ready data foundations: RAG pipelines, vector/semantic retrieval, MCP or similar connector frameworks, or agent-based data access patterns.
Experience with Power BI semantic model development (DAX, M, Tabular Editor) and/or Tableau connectivity and certified data sources.
Experience in sales operations, revenue operations, or go-to-market analytics domains, including territory, pipeline, and quota data models.
Experience with data quality tooling, observability, and automated reconciliation frameworks.
Experience working in contractor-heavy or transition environments, including knowledge capture, code remediation, and acquisition data integration.
Relevant certifications (e.g., Microsoft Fabric, Azure Data Engineer, Salesforce).

Responsibilities

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.
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.
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.
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.
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.
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, and domain teams to gather technical requirements and deliver reliable, well-documented data products.
Mentor and review the work of internal engineers and contractors, raising the engineering bar across the data function.