Managing Director of Data Platform and Engineering (26-27)

IDEA Public Schools • El Paso, TX

2d • $131,000 - $158,500

About The Position

The Managing Director of Data Platform & Engineering leads IDEA’s transformation to a modern, cloud-native data platform that enables rapid insights and self-service analytics across all states and departments. This leader is accountable for the successful delivery and operation of a scalable Snowflake-based data lakehouse, reducing data onboarding from years to days while maintaining operational excellence throughout the transition.

This role is ideal for a visionary technologist who can balance strategic platform leadership with hands-on organizational leadership, driving IDEA toward a nimble data-driven culture that serves students, staff, and stakeholders. The MD of Data Platform & Engineering will sit on the leadership team and report directly to the Vice President of Enterprise Data & Strategy.

Requirements

• Education: Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related technical field
• Experience: Minimum 10 years in data engineering, platform engineering, or data architecture roles with at least 5 years in leadership positions managing technical teams
• Cloud expertise: Demonstrated success implementing cloud data platforms (Snowflake, Databricks, or similar) in production environments
• Modern data stack: Hands-on experience with at least 3 modern data tools (dbt, Fivetran/Airbyte, Great Expectations, etc.)
• Scale: Experience managing data platforms supporting 20+ data sources and hundreds of users across multiple business units or regions
• Team building: Proven track record recruiting, mentoring, and developing data engineering teams from 5-15+ members
• Cloud Data Platform Architecture: Deep expertise in Snowflake architecture, performance optimization, cost management, and security models
• Experience with modern table formats (Iceberg, Delta Lake) and understanding of lakehouse architecture patterns
• Knowledge of cloud infrastructure (AWS/Azure/GCP) including networking, IAM, and service integration
• Understanding of data warehouse modeling techniques (Kimball, Data Vault, wide tables) and when to apply each
• Modern Data Stack & Tools: Hands-on experience with automated ingestion tools (Fivetran, Airbyte, or similar) and ability to evaluate connector reliability
• Proficiency in dbt (data build tool) for transformation including testing, documentation, and package development
• Familiarity with orchestration platforms (Dagster, Prefect, Airflow) for workflow management
• Experience with data quality frameworks (Great Expectations, dbt tests, Monte Carlo) and observability tools
• Knowledge of data cataloging and governance platforms (Atlan, Collibra, Alation)
• DataOps & Software Engineering Practices: Strong foundation in Git workflows, CI/CD pipelines, and infrastructure-as-code (Terraform)
• Experience implementing automated testing, deployment strategies, and rollback procedures for data pipelines
• Understanding of monitoring, alerting, and incident management for data platform operations
• Proficiency in Python and SQL for pipeline development and troubleshooting
• Data Architecture Patterns: Expertise in medallion architecture (Bronze/Silver/Gold layers) and understanding of decoupling strategies
• Experience with data mesh principles for domain-oriented, decentralized data ownership at scale
• Knowledge of semantic layers, metrics stores, and approaches to consistent business definitions
• Understanding of streaming architectures and event-driven patterns (Kafka, Event Hubs) for real-time use cases
• Leadership & Soft Skills Visionary yet pragmatic: Can articulate ambitious 5-year vision while delivering incremental value quarterly
• Change management: Experience leading large-scale technical transformations with legacy system constraints
• Vendor/partner management: Skilled at evaluating, negotiating with, and managing implementation partners and technology vendors
• Influential communication: Can secure executive buy-in and funding for platform investments through compelling ROI narratives
• Bias toward action: Comfortable making decisions with incomplete information and course-correcting rapidly
• Talent magnet: Known for attracting, developing, and retaining top data engineering talent

Nice To Haves

• Advanced degree: Master's degree in related field or MBA with technical focus
• K-12 or education sector: Experience with student information systems (PowerSchool, Focus), assessment platforms, and education data standards
• Multi-state/multi-region: Experience building data platforms for geographically distributed organizations with federated operations
• Certifications: Snowflake certifications (SnowPro Core/Advanced), dbt Analytics Engineering certification, or cloud platform certifications (AWS/Azure/GCP)
• Data mesh implementation: Direct experience implementing data mesh principles in production environments
• Startup/scale-up experience: Track record building data platforms in high-growth environments with rapid iteration
• Public speaking/writing: Conference presentations, blog posts, or open-source contributions demonstrating thought leadership

Responsibilities

• Define and execute a 3-5 year data platform strategy aligned with IDEA's multi-state presence and educational mission
• Drive the transition from legacy on-premises infrastructure to modern cloud data lakehouse architecture on Snowflake
• Establish architectural principles including medallion architecture (Bronze/Silver/Gold), data mesh for state autonomy, and ELT-first patterns
• Champion automation, self-service analytics, and DataOps practices to significantly reduce data onboarding
• Serve as technical thought leader, staying ahead of trends in cloud data platforms, modern data stack tools, and K-12 analytics engineering
• Work in close partnership with the other members of the Enterprise Data & Strategy leadership team
• Makes final decisions on all platform architecture based on recommendations from the Data Platform Architect. Accountable for both architectural outcomes and successful delivery of the platform
• Design and deliver a Snowflake-based data lakehouse architecture that balances performance, cost-efficiency, and strategic flexibility.
• Make informed technical decisions about table formats, transformation patterns, and tool selection based on IDEA's multi-state operations, growth trajectory, and vendor risk tolerance.
• Partner with the Data Platform Architect for deep technical analysis and recommendations, while maintaining final accountability for architectural decisions and their successful implementation.
• Ensure automated ingestion frameworks are implemented using tools like Fivetran or Airbyte to support extensive data sources with minimal manual coding
• Ensure a modern transformation layer using dbt Cloud is implemented with version control, automated testing, and modular design patterns
• Ensure implementation of a comprehensive data quality framework (e.g., Great Expectations, Monte Carlo) with automated validation and anomaly detection
• Ensure deployment of a data catalog and semantic layer in partnership with Business Intelligence and Data Governance, enabling consistent metrics and self-service discovery.
• Define and enforce data governance standards including lineage tracking, security controls, and compliance requirements
• Build, mentor, and grow a high-performing team
• Foster a culture of innovation, continuous improvement, and technical excellence with emphasis on automation and best practices
• Establish clear roles, responsibilities, and career development paths aligned with modern data engineering competencies
• Create operating mechanisms including sprint planning, code reviews, incident response, and knowledge sharing
• Balance maintenance of current production systems with aggressive modernization timelines
• Drive team upskilling in cloud-native technologies, dbt, DataOps practices, and modern data tools
• Sets and resolves DataOps prioritization across Platform Engineering and Analytics Engineering to balance delivery speed, reliability, and automation maturity.
• Partner with Managing Director of Data Science & Analytics, Director of Research and Evaluation, and Director of Business Intelligence to ensure platform serves all analytical needs
• Collaborate with business and academic leaders to understand domain-specific requirements and enable state autonomy through data mesh principles
• Serve as trusted technical advisor to senior leadership, translating complex architecture into business value and ROI
• Build relationships with IT infrastructure, security, and compliance teams to ensure platform meets enterprise standards
• Manage vendor relationships and implementation partner engagements during modernization phases
• Define and track platform SLAs, data quality metrics, and team performance indicators
• Establish measurable goals for data onboarding speed, pipeline reliability, and self-service adoption
• Monitor infrastructure costs and optimize cloud spending through effective resource management
• Ensure audit-ready documentation, disaster recovery capabilities, and enterprise security controls
• Lead incident response for critical data platform issues with clear communication and rapid resolution

Benefits

• Additionally, we offer medical, dental, and vision plans, disability, life insurance, parenting benefits, flexible spending account options, generous vacation time, referral bonuses, professional development, and a 403(b) plan.
• IDEA may offer a relocation stipend to defray the cost of moving for this role, if applicable.

What This Job Offers

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