Deputy CDO

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Deputy Chief Data Officer (Deputy CDO) based in United Arab Emirates. The Deputy Chief Data Officer will play a central leadership role in driving the execution and strategic development of a global data and analytics function within a product-driven technology organization. Acting as the second-in-command to the Chief Data Officer, you will oversee key initiatives spanning data engineering, machine learning, product and marketing analytics, UX research, and experimentation. This position combines strategic leadership with hands-on operational ownership, ensuring that complex cross-functional projects translate into measurable business outcomes. Working directly with C-level executives and business leaders, you will shape priorities, allocate resources, and improve organizational performance. You will also strengthen team leadership, establish effective operating processes, and ensure the reliability and scalability of data platforms. This is an opportunity to grow into a Chief Data Officer position while directly influencing business performance and long-term strategy. Accountabilities The Deputy CDO will take ownership of execution across the Research Center, translating strategic priorities into measurable results while ensuring operational excellence, effective resource management, and strong collaboration across teams. • Strategic Initiative Leadership: Lead selected strategic initiatives from business case development and prioritization through implementation and measurable business impact. Ensure projects align with organizational objectives and deliver tangible value. • Executive Stakeholder Management: Collaborate directly with C-level executives and business leaders to align priorities, negotiate resources and timelines, and manage strategic trade-offs using data-driven arguments and financial insights. • Cross-Functional Project Execution: Oversee major initiatives across data and analytics, including data platform development, BigQuery migration, complex machine learning projects, experimentation processes, AI-powered analytics, UX research, and data quality improvements. • Project Delivery and Issue Resolution: Identify and resolve delivery bottlenecks across teams. Clarify project scope, assign ownership, establish next steps, address dependencies, and escalate risks early to ensure initiatives progress successfully. • Resource and Backlog Management: Manage capacity, resources, and backlogs across multiple teams. Prioritize initiatives according to business value, coordinate dependencies between ML, backend, product, and analytics teams, and reassess or discontinue projects that fail to deliver expected results. • Operational Management and Governance: Establish and maintain the operating framework for the Research Center, including monthly business reviews, quarterly planning, project reviews, retrospectives, commitment tracking, escalation procedures, and centralized performance dashboards. • Business Impact and ROI Measurement: Translate strategic initiatives into measurable business objectives, expected benefits, and financial outcomes. Monitor ROI and actual performance against plans, ensuring that delivery success is measured by business impact rather than task completion alone. • Team Leadership and Development: Support and strengthen team leads across Product Analytics, Data Engineering, Machine Learning, and UX Research. Help them define objectives, prioritize work, improve management practices, provide constructive feedback, and develop their leadership capabilities. • Operational Performance: Oversee the operational health of the data function, monitoring data freshness, platform uptime, ML infrastructure stability, delivery predictability, and experimentation throughput. • Strategic and Organizational Continuity: Act as a trusted partner and successor to the CDO, maintaining the ability to assume responsibility for the entire data function when required. Requirements The ideal candidate is an experienced data and technology leader with a strong background in product organizations, proven operational execution capabilities, and the ambition to progress into a Chief Data Officer position. • At least 7 years of professional experience in data, analytics, or machine learning within product-focused technology companies. • At least 4 years of leadership experience managing highly skilled specialists, managers, and team leads. • Previous experience as a Chief Data Officer, Deputy CDO, Head of Data, or Head of Analytics within a mid-sized or large product organization is highly desirable. • Strong ambition and readiness to grow into a CDO role, combining strategic vision with hands-on responsibility for day-to-day execution. • Broad understanding of data and research functions, including product and marketing analytics, A/B testing, experimentation, machine learning lifecycles, data platforms, data quality, service-level agreements (SLAs), business intelligence, self-service analytics, and unit economics. • Deep practical expertise in at least one major data domain, supported by the ability to understand and coordinate across the wider data ecosystem. • Proven track record of delivering complex, cross-functional data and machine learning initiatives with measurable business outcomes. • Strong operational management skills, including project planning, work decomposition, resource allocation, backlog prioritization, risk management, and coordination of multiple parallel initiatives. • Business-oriented mindset with the ability to evaluate initiatives through financial metrics, expected benefits, ROI, and measurable business impact. • Strong understanding of the trade-offs between delivery speed, quality, business value, and operational risk. • Demonstrated experience working with and influencing C-level executives, supported by strong analytical reasoning, numerical accuracy, and persuasive communication. • Ability to influence stakeholders without relying on formal authority, build trust through expertise, and manage disagreements constructively. • Highly organized, systematic, disciplined, and persistent, with the ability to operate effectively in ambiguous and fast-changing environments. • Excellent communication and interpersonal skills, with the ability to lead distributed and multidisciplinary teams. • Fluent in both Russian and English. Benefits • Remote Work: Fully remote, full-time opportunity with the flexibility to work from anywhere. • Competitive Compensation: Salary range and initial compensation details shared with candidates during the recruitment process, before employment begins. • Annual Leave: 28 calendar days of paid vacation per year. • Wellness Days: 7 additional paid wellness days annually to manage personal matters, rest, and recover without using sick leave. • Family Support: Financial compensation for kindergarten or school fees for employees' children. • Referral Bonuses: Bonuses of up to $5,000 for successfully recommending candidates who join the organization. • Professional Development: 50% reimbursement for professional training, international conferences, and industry events. • Language Learning: Corporate discounts for foreign language lessons. • Health Benefits: Up to $1,000 gross per year per employee toward private health insurance or medical expenses for themselves and eligible close family members when corporate medical insurance is unavailable. • Remote Workplace Support: Provision of an e

At least 7 years of professional experience in data, analytics, or machine learning within product-focused technology companies. At least 4 years of leadership experience managing highly skilled specialists, managers, and team leads. Previous experience as a Chief Data Officer, Deputy CDO, Head of Data, or Head of Analytics within a mid-sized or large product organization is highly desirable. Strong ambition and readiness to grow into a CDO role, combining strategic vision with hands-on responsibility for day-to-day execution. Broad understanding of data and research functions, including product and marketing analytics, A/B testing, experimentation, machine learning lifecycles, data platforms, data quality, SLAs, business intelligence, self-service analytics, and unit economics. Deep practical expertise in at least one major data domain, supported by the ability to understand and coordinate across the wider data ecosystem. Proven track record of delivering complex, cross-functional data and machine learning initiatives with measurable business outcomes. Strong operational management skills, including project planning, work decomposition, resource allocation, backlog prioritization, risk management, and coordination of multiple parallel initiatives. Business-oriented mindset with the ability to evaluate initiatives through financial metrics, expected benefits, ROI, and measurable business impact. Strong understanding of the trade-offs between delivery speed, quality, business value, and operational risk. Demonstrated experience working with and influencing C-level executives, supported by strong analytical reasoning, numerical accuracy, and persuasive communication. Ability to influence stakeholders without relying on formal authority, build trust through expertise, and manage disagreements constructively. Highly organized, systematic, disciplined, and persistent, with the ability to operate effectively in ambiguous and fast-changing environments. Excellent communication and interpersonal skills, with the ability to lead distributed and multidisciplinary teams. Fluent in both Russian and English.

Strategic Initiative Leadership: Lead selected strategic initiatives from business case development and prioritization through implementation and measurable business impact. Executive Stakeholder Management: Collaborate directly with C-level executives and business leaders to align priorities, negotiate resources and timelines, and manage strategic trade-offs using data-driven arguments and financial insights. Cross-Functional Project Execution: Oversee major initiatives across data and analytics, including data platform development, BigQuery migration, complex machine learning projects, experimentation processes, AI-powered analytics, UX research, and data quality improvements. Project Delivery and Issue Resolution: Identify and resolve delivery bottlenecks across teams. Clarify project scope, assign ownership, establish next steps, address dependencies, and escalate risks early to ensure initiatives progress successfully. Resource and Backlog Management: Manage capacity, resources, and backlogs across multiple teams. Prioritize initiatives according to business value, coordinate dependencies between ML, backend, product, and analytics teams, and reassess or discontinue projects that fail to deliver expected results. Operational Management and Governance: Establish and maintain the operating framework for the Research Center, including monthly business reviews, quarterly planning, project reviews, retrospectives, commitment tracking, escalation procedures, and centralized performance dashboards. Business Impact and ROI Measurement: Translate strategic initiatives into measurable business objectives, expected benefits, and financial outcomes. Monitor ROI and actual performance against plans, ensuring that delivery success is measured by business impact rather than task completion alone. Team Leadership and Development: Support and strengthen team leads across Product Analytics, Data Engineering, Machine Learning, and UX Research. Help them define objectives, prioritize work, improve management practices, provide constructive feedback, and develop their leadership capabilities. Operational Performance: Oversee the operational health of the data function, monitoring data freshness, platform uptime, ML infrastructure stability, delivery predictability, and experimentation throughput. Strategic and Organizational Continuity: Act as a trusted partner and successor to the CDO, maintaining the ability to assume responsibility for the entire data function when required.