Product Manager Data & Insights

About Unifonic

Unifonic is a CPaaS (Communication Platform as a Service) company with over 400 employees, dedicated to business communication solutions for more than 5000 customer-centric companies. The company fosters a collaborative work environment and is recognized as a Great Place to Work®. Employees are shareholders in the business.

The Role of Product Manager - Data & Insights

Unifonic is seeking a Product Manager - Data & Insights to lead the development of customer-facing dashboards and internal reporting systems. This full-time role, based in Riyadh, Riyadh Province, requires 5-10 years of experience and involves managing the end-to-end product lifecycle for reporting capabilities, from research and data modeling to delivery and continuous improvement. The Product Manager will collaborate with data engineers, analysts, UX teams, and business stakeholders to build a robust data ecosystem and champion data compliance across all Unifonic products.

Key Responsibilities

• Define and execute the roadmap for customer-facing analytics, reporting portals, and internal reporting infrastructure.
• Align reporting solutions with business goals and customer needs.
• Collaborate with leadership to prioritize initiatives based on impact, complexity, and business value.
• Advise other Product teams on data strategy and design.
• Collect business and technical requirements from cross-functional stakeholders.
• Develop detailed feature requirements, user stories, acceptance criteria, and data contracts for reporting systems.
• Translate ambiguous business questions into well-defined reporting needs and KPIs.
• Partner with data engineers to co-develop data models and data marts in the data warehouse (*, Snowflake, Qlik, Vertica).
• Understand schema design and its impact on performance, usability, and extensibility of data reporting systems.
• Influence architectural decisions around ETL/ELT processes and dimensional modeling.
• Own and enforce data governance standards, including data definitions, taxonomy, lineage, and version control.
• Define processes for data quality verification and anomaly detection.
• Advocate for data accuracy and identify misleading or incomplete insights.
• Own the product experience and lifecycle for reporting and BI platforms (*, Looker, Tableau, Power BI, Sigma, or Mode).
• Define self-service analytics capabilities for internal teams.
• Create feedback loops to improve dashboard usability and relevance.
• Act as a bridge between technical and non-technical teams.
• Present product updates, reporting insights, and metrics to executives and cross-functional teams.
• Facilitate decision-making on trade-offs related to data availability, accuracy, and reporting timelines.

Required Qualifications and Skills

• Ability to define business KPIs and translate business and reporting needs into clear data and product requirements.
• Experience contributing to data governance, data integrity, and compliance initiatives.
• Ability to balance performance, usability, scalability, and cost when designing data and reporting solutions.
• Strong communication and stakeholder management skills, with the ability to translate complex data concepts into clear business requirements and insights.
• Proven ability to lead cross-functional discussions, align stakeholders around data strategy, and communicate reporting goals and insights effectively.

AI Proficiency and Character Traits

Candidates should demonstrate a minimum of L4 (Proficient) AI proficiency, applying AI across product discovery, specification, prototyping, and stakeholder communication. This includes using AI to speed up research, specs, and prototyping while personally owning the accuracy of every output. Hands-on experience with Claude and Lovable is expected, along with the ability to write structured prompts and refine them. The role requires synthesizing insights from various sources, drafting documents with AI as a first-draft partner, and building clickable prototypes. Understanding AI agent construction and identifying automation workflows, as well as flagging AI risks, are also essential.

Key character traits for this role include analytical thinking, problem-solving, ownership, accountability, clear communication, collaboration, adaptability, continuous learning, mentorship, knowledge sharing, resilience, a focus on quality and attention to detail, and advocacy for innovation and best practices.