Senior Manager, Data & AI Delivery
Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Nov 4, 2026.
Senior Manager, Data & AI Delivery
Location: Lake Mary, FL (Hybrid)
Department: Technology, AI & Product Experience (TPX) - Data & AI
Position Type: Full-Time, Exempt
About The IIA
The Institute of Internal Auditors (IIA) is the global leader and recognized authority for the internal audit profession. We advance the profession worldwide through education, advocacy, certifications, standards, and professional resources.
Guided by our values of People First, Integrity, Collaboration, Accountability, and Innovation, we offer a collaborative, purpose-driven workplace where employees can grow professionally while making a global impact.
Why Join The IIA?
Join a global, mission-driven organization where you can help build and scale modern data platforms and artificial intelligence solutions. You'll have the opportunity to lead a growing engineering team, shape reliable data and AI delivery practices, and develop innovative technology capabilities that support The IIA's global operations.
Position Summary
The IIA is seeking a hands-on, results-driven Senior Manager, Data & AI Delivery to lead the execution and operational governance of our enterprise data platform and AI solutions.
This role combines technical leadership, data engineering, AI implementation, and agile delivery management. You'll lead a small team of onshore and offshore engineers, oversee production data pipelines and lakehouse architecture, and ensure data and AI solutions meet established standards for quality, security, reliability, and governance.
The ideal candidate brings strong expertise in SQL, Python, cloud data platforms, data engineering, machine learning (ML), and large language model (LLM) applications, along with experience leading distributed engineering teams and delivering production-ready solutions.
What You'll Do
Data Engineering & Cloud Platform Delivery
Lead the development, deployment, and ongoing support of enterprise data pipelines, lakehouse architectures, and analytics data models.
Remain hands-on with SQL, Python, code reviews, troubleshooting, and technical problem-solving.
Maintain platform reliability through monitoring, incident response, data quality checks, and operational support.
Partner with analytics and business intelligence teams to deliver trusted, well-structured data for reporting and decision-making.
AI & Machine Learning Delivery
Lead the implementation and production deployment of approved AI, machine learning, and LLM-based solutions.
Apply established standards for model evaluation, performance monitoring, reliability, and cost tracking.
Support the integration of AI capabilities into enterprise platforms and business processes.
Identify and resolve technical issues affecting production AI solutions.
Data Governance, Quality & Risk Controls
Implement operational governance standards across data engineering and AI delivery.
Establish and enforce release gates, automated testing, data quality controls, access management, and approval workflows.
Support data lineage, model inventories, evaluation processes, and human review requirements.
Track governance exceptions, technical risks, and control gaps, coordinating remediation with appropriate teams.
Engineering Leadership & Agile Delivery
Lead, mentor, and develop a small team of onshore and offshore data, analytics, and AI engineers.
Manage work intake, sprint planning, backlog priorities, project estimates, and delivery timelines.
Coordinate with Engineering, Technical Product Management, and cross-functional stakeholders on requirements, integrations, and releases.
Oversee implementation vendors and offshore partners, ensuring engineering quality, accountability, and knowledge transfer.
Communicate project progress, technical risks, and delivery priorities to leadership.
Qualifications
Required Qualifications
5+ years of experience in data engineering, analytics engineering, or machine learning engineering, including at least 1 year leading or mentoring engineers as a manager, team lead, or formal technical lead.
Hands-on production experience with modern cloud data platforms and lakehouse architectures, such as Databricks, Apache Spark, Delta Lake, or equivalent technologies.
Strong proficiency in SQL and Python, including active coding, code reviews, and technical troubleshooting.
Experience deploying or supporting production ML or LLM-based solutions, including evaluation, monitoring, and issue resolution.
Experience working with distributed engineering teams, offshore resources, or technology vendors.
Knowledge of agile software delivery, sprint planning, backlog management, and tools such as Azure DevOps or Jira.
Experience with data modeling, data quality, access controls, data catalogs, and lineage.
Demonstrated ability to implement automated testing, release controls, approval workflows, and governance standards.
Strong communication, collaboration, and problem-solving skills, with the ability to explain technical risks and priorities to business stakeholders.
Preferred Qualifications
Experience with Microsoft Azure cloud data and AI services.
Knowledge of MLOps, model registries, CI/CD pipelines, and automated model evaluation.
Experience developing retrieval-augmented generation (RAG) or LLM applications using Azure OpenAI or comparable platforms.
Familiarity with AI governance frameworks, including the NIST AI Risk Management Framework or ISO/IEC 42001.
Databricks, Microsoft Azure, or related data and AI engineering certifications.
Experience in regulated, standards-driven, professional association, audit, risk, or compliance environments.
Equal Opportunity Employer
The Institute of Internal Auditors (IIA) is an Equal Opportunity Employer. We are committed to creating an inclusive workplace where all employees are treated with dignity and respect. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.