Senior Manager, Data & AI Delivery

The Institute of Internal Auditors•Lake Mary, FL

20h•Hybrid

About The Position

The IIA is seeking a hands-on, results-driven Senior Manager, Data & AI Delivery to lead the execution and operational governance of its enterprise data platform and AI solutions. The role combines technical leadership, data engineering, AI implementation, and agile delivery management. The Senior Manager will lead a small team of onshore and offshore engineers, oversee production data pipelines and lakehouse architecture, and ensure data and AI solutions meet standards for quality, security, reliability, and governance. The position involves leading enterprise data pipeline, lakehouse, and analytics data model delivery while remaining hands-on with SQL, Python, code reviews, troubleshooting, and technical problem-solving. It also leads deployment and operational support of approved AI, machine learning, and LLM solutions, and implements governance and risk controls across data and AI delivery. The Senior Manager manages team development, work intake, sprint planning, priorities, estimates, timelines, vendors, and stakeholder communication. The position is based in Lake Mary, Florida, with a hybrid work arrangement. It is a full-time, exempt role in the Technology, AI & Product Experience (TPX) – Data & AI department.

Requirements

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.

Nice To Haves

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.

Responsibilities

Lead 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.
Lead implementation and production deployment of approved AI, machine learning, and LLM-based solutions.
Apply standards for model evaluation, performance monitoring, reliability, and cost tracking.
Support integration of AI capabilities into enterprise platforms and business processes.
Identify and resolve technical issues affecting production AI solutions.
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, and coordinate remediation with appropriate teams.
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.