Principal AI Data Engineer

Presidio • Austin, TX

7h

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.

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.

Benefits

• Presidio is committed to hiring the most qualified candidates to join our amazing culture.
• We aim to attract and hire top talent from all backgrounds, including underrepresented and marginalized communities.
• We encourage women, people of color, people with disabilities, and veterans to apply for open roles at Presidio.
• Diversity of skills and thought is a key component to our business success.
• Presidio has a strong commitment to the community we serve and our employees.
• As an Equal Opportunity Employer, we strive to have a workforce that includes the community we serve.
• Presidio is committed to working with and providing reasonable accommodations to individuals with disabilities.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal