Job Summary: 

The Director of Global Data Engineering plays a critical role in the global data organization by leading and shaping our engineering efforts. The director will oversee the design, development, and maintenance of our infrastructure that support analytics needs across the company. In addition, the director will work extensively with the engineers, architects, and analysts to provide insights and drive decision making in the Global Data department. This is a leadership position that requires a strong blend of deep technical expertise and exceptional leadership skills.

Job Expectations:

Engineering Leadership:

• Provide strategic direction and technical leadership to the engineering teams, ensuring the development of scalable, robust, and high-quality software solutions.

• Foster a culture of innovation, collaboration, and continuous improvement within the engineering organization.

• Develop and implement engineering best practices, standards, and processes to drive efficiency and quality across all projects.

• Collaborate with cross-functional teams, including product management and design, to ensure successful product development and delivery.

• Ensure engineering work is aligned with the larger departmental and organizational goals and that data needs are being met across the company.

• Understands and optimizes the total cost of ownership of services within the department.

Global Data Oversite:

• Define and execute data acquisition and data integration strategies.

• Identify opportunities to leverage data for insights, innovation, and business growth.

• Promote and uphold data quality and compliance standards across the organization.

• Collaborate with other leaders in the global data organization to identify opportunities for improvement of the global data organization.

• Lead initiatives to enhance data infrastructure, data management systems, and data analytics capabilities.

Team Management:

• Recruit, develop, and retain top engineering talent, providing mentorship and guidance to team members.

• Set clear goals, expectations, and performance metrics for the engineering teams, and provide regular feedback and performance evaluations.

• Foster a collaborative and inclusive work environment that promotes diversity, teamwork, and personal growth.

• Promote a culture of knowledge sharing and continuous learning within the engineering organization.

Stakeholder Engagement:

• Communicate technical concepts and strategies effectively to non-technical stakeholders.

• Manage relationships with external partners, vendors, and contractors to ensure successful project delivery.

The duties and responsibilities described above may provide only a partial description of this position. This is not an exhaustive list of all aspects of the job. Other duties and responsibilities not outlined in this document may be added as necessary or desirable, with or without notice.

Knowledge, Skills and Abilities:

• Strong understanding of emerging technologies, industry trends, and best practices in data management.

• Knowledge of relational and non-relational data structures, theories, principles, and best practices.

• Understanding of data lifecycles, data computation principles, data stores and a solid understanding of CI/CD principles.

• Experienced in Agile methodologies & DevOps approach to maintaining pipelines and databases.

• Proven track record in designing and implementing self-service data platforms, enabling business users to access and analyze data independently with minimal technical assistance.

• Strong expertise in integrating and managing data workflows involving SAP systems, including experience in the extraction, transformation, and loading (ETL) of data to and from SAP environments to support enterprise-wide data solutions.

• Demonstrated ability to optimize resource allocation and costs, coupled with expertise in architecting scalable data solutions using modern design patterns to enhance performance and reduce operational expenses.

• Knowledge of data privacy regulations (GDPR, CCPA, CRPA) and the impact these regulations have on data engineering framework.

• Have an ability to prioritize workload and handle multiple programs and at times meet tight deadlines.

• Strong problem-solving and analytical skills.

• Strong facilitation and consensus building skills. 

• Strong oral and written communication skills; Ability to communicate by simplifying complexity.

• Excellent ability to communicate large-scale projects and their impact on other decisions.

• Be proactive, requiring minimal supervision with strong time management or organization skills.

• Proven experience leading large scale projects in an engineering team.

Equipment Knowledge:

• Extensive experience with Databricks (DLT, Medallion Architecture, Lakehouse Concepts, etc) and Amazon Web Services (AWS) with a deep understanding of their respective data services and offerings.

• Strong proficiency in cloud-based data storage solutions such as Amazon S3 and Google Cloud Storage, including data ingestion, data organization, and data lifecycle management.

• In-depth knowledge of Tableau's data architecture and engineering capabilities, with proven experience in optimizing Tableau for high performance and efficient data processing across large datasets

• Knowledge of data governance and security practices specific to Databricks, AWS and Google Cloud, including IAM (Identity and Access Management), encryption, and access control.

• Knowledge of Databricks and AWS machine learning and AI services, such as Databricks MLFlow and Amazon SageMaker, for leveraging data insights and building intelligent applications.

• Understanding of cost optimization strategies and techniques specific to Databricks, AWS and Google Cloud, including resource provisioning, autoscaling, and leveraging spot instances.

• Experience with Google Business Suite (Gmail, Drive, Docs, Sheets, Forms) preferred.

 Experience Requirements:

•  Generally requires a minimum of 10 years or more years of related work experience. At least 5 years of experience managing a data engineering team within a data and analytics organization, including establishing solution architectures and building an executable backlog for the team.

• Strong technical background with expertise in data management, and data analytics.

• Demonstrated success in driving engineering innovation and delivering high-quality products. 

Education Requirements: 

Bachelor or Master's degree in Computer Science, Information Systems, or related fields preferred, or a combination of education and equivalent work experience. An advanced degree is a plus.

Judgment/Reasoning Ability: Able to identify, troubleshoot and resolve problems quickly using sound judgment, poise and diplomacy. Ability to use judgment and reasoning skills, and determine when to escalate issues, as required, in a timely manner.

Physical Demands: The physical demands described here are representative of those that must be met by a Team Member to successfully perform the essential functions of this job. While performing the duties of this job, the Team Member is regularly required to talk and hear. The Team Member is frequently required to sit, walk, climb stairs, use hands and fingers, bend, stoop and reach with hands and arms. Reaching above shoulder heights, below the waist or lifting as required to file documents or store materials throughout the work day. The Team Member may occasionally lift or move office products and supplies up to 25 pounds. Proper lifting techniques required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Work Environment: The noise in the work environment is usually moderate. Other factors are:

• Hectic, fast-paced with multi-level distractions

• Professional, yet casual work environment

• Office / Warehouse environment

• Ability to work extended hours as required

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