VP of Data Engineering

Vice President, Data Engineering

Leadership and Strategy Responsibilities

• Define Data vision, strategy, governance, and associated practices. Lead the creation (and assure the ongoing relevance) of the organization’s data strategy to enable data management, stewardship, governance, analytics, and best-in-class AI function in collaboration with Platform Leaders, Office of the CIO, and Customers.
• Build and lead the Data organization from the ground up, fostering a data-driven culture and overseeing career development and performance management for a team of analysts, engineers, and architects.
• Build strong working relationships with key business leaders to establish the vision for managing data as a business asset — to exploit data and analytics capabilities to maximize the value derived from data assets.
• Provide technical vision and leadership to build complex and sustainable data ecosystems that will be used across all Platforms.
• Lead Analytics, BI, and Visualization from data stores and data lakes for the client analytics offices.
• Lead and manage data science, data modeling, machine learning, AI Ops, and ML Ops service offerings to our customers.
• Productize data science, management, governance, privacy, and analytics as an offering to our large institutional customers.
• Oversee the implementation of solutions for tracking data quality, data consistency, and lineage.
• Build reference data practice that can be adopted across customers and higher education institutions.
• Collaborate with internal teams to find areas of opportunities to expand the scope of data engineering with automation and machine learning.
• Streamline the entire data ecosystem from end to end to ensure the most efficient, standard, privacy-compliant processes possible.
• Develop strong relationships with data science, engineering, and business partners, and improve how we leverage data. Institute an enterprise operating model for data and analytics which is consistent with the capabilities and competencies required to execute the strategy.
• Identify new kinds, types, and sources of trusted data to enable business value innovation throughout the organization.
• Create and oversee a centralized data management service for internal and external data to ensure quality, traceability, timeliness, usability, and cost-effectiveness.
• Define processes for the effective, integrated introduction of new data.
• Provide thought leadership around implementation solutions and controls to enable data privacy, compliance, and security as required by state laws and mandates.
• Foster the creation of a data-driven culture, related competencies, and data literacy across the enterprise. Lead these transformation efforts by developing D&A talent and maturing the capability of the organization.
• Oversee delivery models, methods, and practices for creation of data products and to ensure consistent application and use of data and analytics solutions.
• Establish and maintain trust in data assets by instituting governance mechanisms for data and algorithms used for analysis, analytical applications, and automated decision-making.
• Lead data-driven innovation for the enterprise, including investigation, adoption, and exploitation of artificial intelligence.

Business Value and Outcomes

• Identify internal and external stakeholders and their goals. Organize, formalize, and prioritize your stakeholders and their wants and needs.
• Express business outcomes in terms of your stakeholders (e.g., in terms of revenue growth, or cost and risk reduction).
• Focus on business outcomes, not reporting. Inventory the business’ KPIs, not just to report on the business with dashboards and reports. Modern data and analytics strategies use KPIs that describe how data and analytics will be used to improve the business, achieving those business outcomes through specific business actions.
• Create an optimal portfolio of business outcomes, contributing the essential data and analytics capabilities necessary to run a successful organization.
• Connect with stakeholders to identify stakeholders’ unmet needs that expose new opportunities, and to redesign business processes and interactions as part of the digital transformation.

Management and Operations

• Develop, manage, allocate, and govern the annual budget for Data organization.
• Organize and lead a data and analytics center of excellence, and constantly improve the organization’s capacity to develop insights with advanced analytics.
• Define members’ responsibilities and accountabilities. Define job roles, recruit candidates, and then manage (directly or indirectly) a team of data and analytics leaders and senior information management professionals in different regions or business units.
• Oversee development, publishing, and maintenance of the organization’s data and analytics architecture and platforms, as well as a roadmap for its future development, ensuring that it matches and supports business needs.

MINIMUM QUALIFICATIONS, EDUCATION, KNOWLEDGE, SKILLS, AND ABILITIES:

Education & Experience

• At least a Bachelor’s degree; Master’s degree preferred.
• 15 years of prior experience in leading and scaling a data engineering organization, and overall data and analytics functions (e.g. data planning, data governance, data management, data visualization, machine learning, AI, etc).
• Significant experience with industry-standard platforms and tooling including AWS, relational and non-relational solutions, Databricks, Snowflake, Qlik, Tableau Alteryx, etc.
• Experience leading geographically distributed teams.
• Hands-on working experience in architecting, building, and operating data platforms is preferred.
• Comfortable in presenting to both business and technical executives, exceptional communication skills
• Prior experience in managing data practices at large organizations with 100’s of data sources and 50-100 TB scale.
• At least 10 years of leadership experience in technology organizations
• Significant experience with industry-standard platforms and tooling including AWS, Azure, Data Cloud, relational and non-relational solutions, Databricks, Snowflake, Qlik, Tableau, etc.
• 5+ years of leadership experience in leading cross-functional teams, multidisciplinary environments, and/or enterprise-wide data and analytics programs, operating and influencing effectively across the organization and within complex contexts.
• Prior experience in developing open-source and in-house data platforms and analytics solutions is nice to have.
• Experience in integrating complex, cross-corporate processes, and information strategies, and/or designing strategic metrics and scorecards.
• Background in CRM, ERP, e-commerce, edtech, strategy, and management consulting preferred.

Knowledge, Skills & Abilities

• Demonstrated leadership skills and experience leading complex, multidisciplinary talent teams in new endeavors and delivering solutions.
• Excellent business and data acumen and interpersonal skills.
• Business process transformation by being able to understand how data and analytics can help redesign the way work is done and track how impactful the changes are.
• Influencing and emotional intelligence: For example, by asserting ideas and persuading others to gain support across an organization or to adopt new behaviors.
• Creative and critical thinking, and outstanding analytical and problem-solving abilities
• Ability to deliver by working with business domain teams as well as with technical staff.
• Strong talent management skills including talent development, coaching, mentoring, and team motivation.
• Adaptability and ability to manage rapid change in business needs, processes, or technologies.