Director of Data Engineering & AI Enablement

Arizona Department of Education-posted 29 days ago
Full-time • Director
Hybrid • Scottsdale, AZ
251-500 employees
Machinery Manufacturing
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Information Technology Services (ITS) at the MCCCD seeks a dynamic Director of Data Engineering & AI Enablement. Reporting to the CIO, you'll craft the strategy and practical architecture for modern data platforms, drive AI adoption, and forge a data-driven culture that delivers real-world impact in student services, institutional effectiveness, and district operations. This new position is a transformative opportunity to become a key part of our expanding organization: you will build a team from the ground up, evaluate and integrate AI tools and services, and shepherd AI/ML use cases from ideation to production-while establishing ethical and governance guardrails. At the same time, you will lead the modernization of MCCCD's data engineering and business intelligence ecosystems, including dashboarding, data warehousing, data lakes, and robust enterprise database infrastructure. You'll define long-term roadmaps, budget and resource plans, and partner with leadership across colleges to align technical strategy with educational mission. If you are energized by leading innovation, shaping outcomes at scale, and coaching high-performing teams, this role offers an unmatched chance to build the future of AI and data at MCCCD. The ideal candidate for Director is someone who possesses tactical and influential skills, with deep technical expertise working with python and data transformations, structured and unstructured data, and building a team. If you're looking to advance to the next stage in your Data/AI career, apply to this role today. Relocation assistance will be considered for the selected candidate.

  • Plan, organize, assign, supervise, review, and evaluate work of staff.
  • Identify training needs and promote professional growth.
  • Build, lead, and develop a team of data and AI professionals-fostering collaboration, innovation, and agility.
  • Hold team members accountable for execution and deliverables.
  • Lead recruiting, onboarding, and retention strategies for AI/data engineering talent.
  • Mentor staff on technical best practices, career progression, and cross-functional engagement.
  • Lead multiple cross-functional teams supporting AI, data engineering, business intelligence, and enterprise databases.
  • Design, build, and maintain scalable data infrastructure for analytics, operational systems, and AI applications.
  • Define district-wide data strategy in collaboration with institutional research and academic leadership.
  • Ensure alignment with data governance, security, and privacy best practices.
  • Evaluate architectures (lakehouse, data mesh, hybrid cloud) and select frameworks suited for AI at scale.
  • Oversee integration with student information systems (SIS), LMS platforms, and other education ecosystems.
  • Direct the design and maintenance of enterprise data warehouses, data lakes, and semantic layers for BI use cases.
  • Ensure the scalability, availability, and performance of centralized database systems, including both structured and unstructured data stores.
  • Lead the development and governance of business intelligence environments, dashboards, and self-service reporting platforms (e.g., Tableau, Power BI).
  • Oversee foundational architecture to support LLM and GenAI infrastructure, including prompt pipelines, embeddings, and secure data routing.
  • Identify and drive AI/ML use cases district-wide, including prototypes, POCs, production models.
  • Promote ethical AI practices: bias mitigation, interpretability, monitoring, and transparency.
  • Manage end-to-end MLOps-including feature stores, model registry, CI/CD, and observability metrics.
  • Evaluate and deploy GenAI tools, pretrained models, and third-party services to accelerate value.
  • Partner with academic innovation teams to evaluate the use of LLMs in tutoring, instructional design, and administrative workflows.
  • Direct LLM architecture, including embedding strategies, vector databases, and retrieval-augmented generation (RAG) systems.
  • Collaborate with cloud engineers and data scientists to operationalize AI workloads securely and at scale.
  • Serve as liaison between district ITS, college IR/data teams to align goals and tools.
  • Establish shared data definitions, pipelines, and interoperability district-wide.
  • Create communities of practice to build data literacy and AI readiness across staff and faculty.
  • Lead workshops and training on data engineering, AI use cases, and governance essentials.
  • Facilitate district wide collaboration on shared BI tools, enterprise dashboards, and institutional reporting requirements.
  • Manage a budget of tools and resources within policy.
  • Participate in and may lead shared governance efforts.
  • Lead District-wide Data Governance efforts.
  • Define budget strategies and ROI metrics for AI investments and vendor engagements.
  • Oversee contracts, renewals, and vendor partnerships for BI platforms, cloud data tools, and database technologies.
  • Develop performance monitoring strategies for enterprise databases, ETL jobs, and data delivery SLAs.
  • Performs related duties as assigned
  • Bachelor's degree from a regionally accredited institution in any field and six (6) years of professional experience in area related to the intended assignment, which includes two (2) years of supervisory or management experience.
  • An equivalent combination of the conferred degree, education, certification and directly related full-time work experience as described above sufficient to successfully perform the essential duties of the job such as those listed above, unless otherwise subject to any other requirements set forth in law or regulation, may be considered.
  • Demonstrated experience selecting and implementing automated data ingestion pipelines-e.g. leveraging tools like Apache Airflow, dbt, Kafka, Spark, Azure Data Factory.
  • Proven success leading predictive analytics or AI strategies at an organizational scale-turning data into actionable insights across divisions or campuses.
  • Expertise building data platforms in cloud environments-AWS, Azure, and/or GCP-leveraging data lakes, lakehouses, and modern warehousing patterns.
  • Experience integrating data engineering and AI workflows with SIS, Learning Management Systems, or EdTech platforms to support learning analytics or student interventions.
  • Skilled in translating complex technical capabilities into roadmaps aligned with institutional business and educational goals.
  • Experience building or upskilling a diverse team to enable the transition from legacy tools and applications to a cloud-based, AI focused platform.
  • Hands-on expertise with data pipeline development using SQL, Python, Spark, dbt, or cloud-native tools.
  • Experience designing and maintaining enterprise data warehouses and data lakes for real-time and batch workloads.
  • Knowledge of BI and dashboarding platforms (e.g., Power BI, Tableau, Looker), with experience creating centralized, self-service analytics environments.
  • Demonstrated success overseeing large-scale relational and NoSQL databases (e.g., PostgreSQL, Snowflake, BigQuery, MongoDB), including database health monitoring, scalability, and recovery.
  • Understanding of and experience with database administration in a cloud environment (e.g. AWS RDS) including database migration, health and performance monitoring, and backup and recovery.
  • Familiarity with data cataloging, metadata management, and semantic modeling.
  • Experience developing architecture to support LLM applications, including vector databases (e.g., FAISS, Pinecone), prompt chaining, and token optimization strategies.
  • Proficiency with AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn; and experience with production MLOps best practices (model registry, feature store, continuous deployment)
  • Deep understanding of data governance, privacy, compliance, and security in public-sector or higher-ed contexts-particularly FERPA, HIPAA, and student data privacy.
  • Familiarity with privacy-enhancing technologies (PETs), such as differential privacy, anonymization, or federated learning.
  • Experience presenting technical strategy and ROI to executive leadership, board members, or shared governance bodies.
  • Strong skills in bridging technical and non-technical stakeholders using data visualization/BI tools to create clarity and buy-in.
  • Exceptional leadership, strategic thinking, and cross-functional collaboration-capable of influencing district stakeholders and creating communities of practice.
  • Master's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field preferred.
  • Experience leading large-scale data modernization or AI adoption initiatives in multi-campus organizations, including vendor selection and management.
  • Proven budget oversight, strategic planning, and P&L or ROI management experience tied to AI/technology investments.
  • Nationwide Medical, Dental, and Vision Coverage
  • Paid Time Off: Vacation, Sick Leave, and Personal Time
  • 20 Paid Observed Holidays
  • Company-paid Life Insurance, AD&D, and Short-Term Disability plans, with the option to purchase supplemental coverage
  • https://www.azasrs.gov/, including Long Term Disability and Retiree Health Insurance with 100% employer-matching contributions
  • Optional Retirement Plans: 403(b), 457(b), Roth 403(b), Roth 457(b)
  • Tuition Reimbursement for employees and dependents
  • Annual Professional Development Funding
  • Flexible Work Schedules
  • District-Wide Wellness Program with Workshops and Webinars
  • Monthly Health & Wellness Calendar and Newsletter
  • Virta Diabetes Reversal Program, Support Groups, and Diabetes Empowerment Education Programs
  • Employee Assistance Program (EAP)
  • Sight-On-Site Eye Care Services
  • Mobile On-Site Mammography Screenings
  • Pre-Retirement Planning Events
  • Qualifying Employer forhttps://studentaid.gov/manage-loans/forgiveness-cancellation/public-service (potential loan forgiveness for federal Direct Loans after meeting repayment requirements and working full-time for an eligible employer)
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