Product Manager Data & Insights

About Unifonic and the Role

Unifonic, recognized as a Great Place to Work®, operates in the CPaaS (Communication Platform as a Service) sector, focusing on business communication solutions. The company serves over 5000 customer-centric companies with a team of over 400 employees. This full-time position for a Product Manager - Data & Insights is based in Riyadh, Riyadh Province, and requires 5-10 years of experience.

Role Overview

The Product Manager, Data & Insights, will lead the development of both customer-facing dashboards and internal reporting systems. This role integrates product management, data engineering, and analytics to deliver intuitive, reliable, and scalable reporting experiences for external users and internal stakeholders. Responsibilities include owning the end-to-end product lifecycle for reporting capabilities, from customer research and data modeling to delivery, governance, and continuous improvement. The role also involves partnering with data engineers, analysts, UX teams, and business stakeholders to build a robust data ecosystem that supports self-service analytics, compliance, and executive decision-making. Additionally, the Product Manager will champion 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, prioritizing 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 (Finance, Operations, Customer Success, Product, Engineering).
• Develop detailed feature requirements, user stories, acceptance criteria, and data contracts for reporting systems.
• Translate business questions into 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 related to 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.
• Ensure data accuracy and identify misleading or incomplete insights.
• Manage 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 and create feedback loops for continuous improvement of dashboards.
• Act as a liaison between technical and non-technical teams, translating business needs into technical requirements.
• 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 Requirements

• Experience: 5+ years in product management, data, analytics, or reporting platforms, with a focus on data-driven products.
• Reporting Expertise: Proven experience delivering customer-facing reporting products, dashboards, and internal analytics solutions.
• Data Governance: Strong understanding of data privacy, governance, and compliance frameworks, including GDPR and PDPL.
• Collaboration: Experience working cross-functionally with data engineers, analysts, product teams, UX, and business stakeholders.
• Technical Skills: Hands-on experience with data warehouses (*, ClickHouse, Vertica, Snowflake) and BI tools (*, Metabase, Tableau, Looker, Power BI, Sigma). Strong SQL skills and understanding of data modeling concepts (fact and dimension tables, star schemas).
• Data Pipelines: Familiarity with ELT pipelines, dbt, and data orchestration tools is beneficial.
• Data Quality: Understanding of data quality, integrity, scalability, and performance considerations in reporting solutions.
• Requirements Definition: Ability to define business KPIs and translate business and reporting needs into clear data and product requirements.
• Leadership: Proven ability to lead cross-functional discussions, align stakeholders on data strategy, and communicate reporting goals and insights effectively.
• Education: Bachelor’s degree in Computer Science, Data Science, Business, Engineering, or a related field.

AI Proficiency and Toolkit

• AI Application: Proficient (L4) in applying AI across product discovery, specification, prototyping, and stakeholder communication, using AI to speed up research, specs, and prototyping while owning output accuracy.
• AI Tools: Hands-on experience with Claude and Lovable, writing structured prompts, and refining prompts.
• Data Synthesis: Synthesizing interviews, tickets, and reviews into themes and insights, validating against source data.
• AI-Assisted Documentation: Drafting PRDs, user stories, and executive updates with AI as a first-draft partner, verifying all claims.
• Prototyping: Building clickable prototypes in Lovable for idea validation, clarifying that prototypes are not production-ready.
• AI Understanding: Understanding how AI agents are built and identifying workflows for automation, flagging AI risks (hallucination, bias, data privacy).
• Technical Toolkit: Strong SQL and data analysis skills, experience with data warehouses (Snowflake, ClickHouse, Vertica), knowledge of data modeling, dimensional modeling, data marts, and data pipelines. Experience with BI and reporting tools (Looker, Tableau, Power BI, Sigma, Metabase). Understanding of ETL/ELT, dbt, data orchestration, data governance, quality, lineage, and compliance (GDPR, PDPL).

Professional Attributes

• Analytical Thinking: Ability to approach problems clearly, use data, and find solutions.
• Ownership: Takes responsibility and follows projects through to completion.
• Communication: Explains ideas clearly and listens to others across teams.
• Collaboration: Works effectively with others and supports team goals.
• Adaptability: Adjusts to change and continuously learns new skills or tools.
• Mentorship: Helps others grow and shares knowledge.
• Resilience: Maintains composure under pressure and handles setbacks constructively.
• Quality Focus: Performs work carefully and strives for continuous improvement.
• Advocacy: Encourages best practices, efficiency, and new ideas.