Product Manager Data & Insights

About Unifonic

Unifonic is a CPaaS (Communication Platform as a Service) company with a team of over 400 professionals. The company serves more than 5000 customer-centric organizations, focusing on business communication solutions. Unifonic fosters a collaborative work environment.

Role Overview

Unifonic is seeking a Product Manager - Data & Insights to join its team in Riyadh, Riyadh Province. This full-time role involves leading the development of both customer-facing dashboards and internal reporting systems. The position combines product management, data engineering, and analytics to deliver intuitive, reliable, and scalable reporting experiences for external users and internal stakeholders. The Product Manager will own the end-to-end product lifecycle for reporting capabilities, from customer research and data modeling to delivery, governance, and continuous improvement. This role also involves championing data compliance initiatives 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 in data strategy and design.
• Collect business and technical requirements from cross-functional stakeholders, including Finance, Operations, Customer Success, Product, and Engineering.
• 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 closely 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 extract-transform-load (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 in collaboration with engineering and analytics teams.
• Advocate for data accuracy and act as a watchdog for 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 continually improve dashboard usability and relevance for both internal and external users.
• Act as the bridge between technical and non-technical teams, translating business needs into technical requirements and vice versa.
• 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.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Data Science, Business, Engineering, or a related field.
• 5-10 years of relevant experience.
• AI Proficiency: Minimum Level: L4 (Proficient). This includes applying AI across product discovery, specification, prototyping, and stakeholder communication.
• Expected to use AI to speed up research, specifications, and prototyping, while personally owning the accuracy of every output.
• Hands-on experience with Claude and Lovable.
• Ability to write structured prompts (role, context, constraints, format) and refine prompts rather than manually patching outputs.
• Capable of synthesizing interviews, tickets, and reviews into themes and testable insights, and validating every insight against source data before sharing.
• Proficient in drafting PRDs, user stories, and executive updates with AI as a first-draft partner, then verifying every claim and number before it reaches leadership.
• Ability to build clickable prototypes in Lovable to validate ideas with stakeholders, with clear communication that a prototype is not production-ready.
• Understanding of how AI agents are built (prompts, tools, memory, guardrails) and proactive identification of workflows suitable for automation.
• Awareness of AI risks (hallucination, bias, data privacy, over-reliance) and knowledge of what should and should not be AI-assisted.