Head of Engineering Operations

The Head of Engineering Operations is a highly analytical and strategic role crucial utilized to ensure engineering processes are efficient, scalable, and aligned with the company's global and regional strategic objectives. This role focuses on establishing and leveraging engineering data to optimize the efficiency of the global and regional engineering department. emphasis on data analysis, performance measures, and strategic insights. This role would include, but not limited to, the following responsibilities:

Strategic Leadership:

• Establish and implement a strategy for collecting and managing engineering data across the organization, ensuring data integrity, accuracy, and accessibility.
• Work with various teams to integrate data from different sources (e.g., software development, hardware production, testing, deployment, etc.) for centralizedanalysis.
• Establish data governance policies to maintain consistency, quality, and compliance with relevant regulations and standards.
• Proven ability to lead and manage an operations team.

Drive Results and Operational Excellence:

• Establish and track key performance indicators (KPIs) related to engineering efficiency, such as productivity, cycle times, resource utilization, and defect rates.
• Conduct in-depth analysis of engineering data to identify trends, inefficiencies, and opportunities for improvement at both global and regional levels.
• Compare the organization's engineering efficiency against industry benchmarks and competitors to identify areas for enhancement.
• Strong expertise in data analysis, a deep understanding of engineering processes, and the ability to translate data insights into actionable improvements.
• Strong analytical and problem-solving skills, with the ability to identify issues, assess risks, and develop effective solutions.

Compliance and Regulatory Oversight:

• Assess engineering performance on a global scale, identifying patterns and trends that affect overall efficiency across different regions.
• Analyze regional data to understand how local factors, such as regulations, market conditions, and workforce capabilities, impact engineering efficiency.
• Identify regional best practices that can be scaled or adapted for use in other regions or globally.

Reporting and Risk Management:

• Create and maintain dashboards that provide real-time visibility into engineering performance, efficiency metrics, and trends across the organization.
• Prepare detailed reports and presentations for Engineering Leadership Team as well as Programs, highlighting key insights, risks, and recommendations for improving efficiency for each of the various disciplines.
• Use data visualization techniques to clearly communicate complex data insights to stakeholders at all levels of the organization.

Change Management and Operational Efficiency:

• Establish collaboration with Quality department to conduct root cause analysis to identify the underlying factors contributing to inefficiencies and develop targeted improvement initiatives.
• Work with the various engineering departments to implement continuous improvement initiatives based on data-driven insights, aiming to enhance productivity and reduce waste.
• Provide data-drivensupport for change management initiatives, helping to ensure that process changes are effective and aligned with strategic goals.

Collaboration and Influencing:

• Collaborate with various departments (e.g., engineering, program management, and O&M) to ensure that data collection and analysis processes are aligned with broader organizational goals.
• Engage with regional and global stakeholders to understand their specific data needs and ensure that efficiency analyses are relevant and actionable.
• Establish a data-driven sharing model for insight and best practice sharing across various regions and departments to foster a culture of continuous improvement.
• Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Automation and Analytics:

• Utilize advanced analytics, including predictive modeling, to forecast future performance trends and identify potential efficiency gains.
• Implement and manage advanced analytics tools and platforms that enhance the organization's ability to analyze engineering data and drive efficiency improvements.
• Explore opportunities to automate data collection and analysis processes to improve accuracy and reduce the time required for generating insights.

Qualifications:

• Education - Bachelor's degree in engineering, Information Technology, Computer Science, or a related field. Master's degree preferred.
• Experience - Minimum of 10 years of experience in engineering roles, with at least 5 years in a leadership role, preferably in the payment industry.

Cubic Pay Range:

$150,000 - 180,000 + benefits.

The Cubic pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.

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