Product Manager Data & Insights

📣 إعلان

منذ ساعتين تقريبا

	نوع العقد	دوام كامل
	طبيعة الوظيفة	بالموقع
	الموقع	الرياض

وصف الوظيفة

About Unifonic

Unifonic is a CPaaS (Communication Platform as a Service) company based in Riyadh, Riyadh Province, recognized as a Great Place to Work®. The company focuses on business communication solutions, supported by a team of over 400 employees serving more than 5000 customer-centric companies. Unifonic fosters a collaborative work environment that encourages creativity.

The Role of Product Manager - Data & Insights

Unifonic is seeking a Product Manager - Data & Insights for a full-time position. This role involves leading the development of both customer-facing dashboards and internal reporting systems. The Product Manager will be responsible for the end-to-end product lifecycle for reporting capabilities, from customer research and data modeling to delivery, governance, and continuous improvement. This position requires 5-10 years of experience and involves close collaboration with data engineers, analysts, UX teams, and business stakeholders to build a robust data ecosystem.

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.
Act as an advisor for 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 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 monitor 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.

Required Qualifications and Skills

5-10 years of experience in a relevant field.
Experience applying AI across product discovery, specification, prototyping, and stakeholder communication.
Ability 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.
Proficiency in writing structured prompts (role, context, constraints, format) and refining prompts rather than manually patching outputs.
Ability to synthesize interviews, tickets, and reviews into themes and testable insights, validating every insight against source data.
Experience drafting PRDs, user stories, and executive updates with AI as a first-draft partner, then verifying every claim and number.
Skill in building clickable prototypes in Lovable to validate ideas with stakeholders, with clear understanding that prototypes are 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.