Product Manager - Unified Entity Layer

Meltwater; Cape Town; On-site; ZAR 800 000 - 1 200 000; Full time

Job summary

A data-focused technology company in Cape Town is seeking a Product Manager to lead the evolution of their core data foundation. You will unify data entities, streamline data reliability, and collaborate across teams to enhance product experiences. Ideal candidates have 4+ years in product management with a strong understanding of structured data, and the ability to translate complex concepts into actionable product features. This role offers an opportunity to make substantial improvements to data efficiency and user insights.

Qualifications

• 4 years+ in Product Management with data-heavy systems.
• Strong technical understanding of structured data and APIs.
• Experience with entity models or structured data relationships.

Responsibilities

• Own and evolve the core entity data model.
• Unify and standardize datasets to improve reliability.
• Collaborate with AI & Enrichment teams to enhance classification.

Skills

Product Management

Data modeling

Communication skills

Collaboration

Structured thinking

Understanding of data-heavy systems

Job description

Job title : Product Manager - Unified Entity Layer

Job Location : Western Cape, Cape Town Deadline : December 12,

What We’re Looking For :

We’re looking for a Product Manager to lead the evolution of our core data foundation — the structured layer that connects and contextualizes information across Meltwater’s ecosystem.

You’ll shape and expand the semantic models that ensure key entities (such as organizations, channels, themes, etc.) are consistently identified and linked across our products.

Your work will enable more meaningful insights, better discovery, and clearer understanding for our users by making data relationships transparent, reliable, and easy to work with.

This role sits at the intersection of data architecture, enrichment, and product experience, translating complex data structures into intuitive, high-value capabilities for customers.

What You’ll Do :

Own and evolve the core entity data model that connects key entities across our products (e.g., organizations, sources, themes).

Unify and standardize datasets to reduce duplication and improve consistency and data reliability.

Define how entities are identified, matched, and maintained, combining automated methods with human review where needed.

Work closely with AI & Enrichment teams to ensure entity context improves classification, sentiment, topics, and narrative understanding.

Collaborate with Search & Retrieval teams to ensure fast, reliable entity-aware search and filtering experiences.

Set and monitor data quality measures, and drive ongoing improvements in accuracy, coverage, and consistency.

What You’ll Bring :

4 years+ in Product Management working with data-heavy systems - for example, data platforms, ML / NLP enrichment systems, or large-scale information architectures.

Customer value mindset : You understand how foundational data capabilities power end-user workflows, and you work closely with customer-facing PMs to translate platform capabilities into meaningful product improvements and outcomes.

Comfort working across teams : You collaborate confidently with engineering, data science, and other product teams.

Structured thinking : You enjoy making sense of complex data and building clear frameworks instead of isolated features.

Excellent communication skills : You can effectively translate data structure concepts into tangible product value for both internal and external stakeholders.

Strong technical understanding of structured data, APIs, and data modeling (you can converse comfortably with engineers and data scientists).

Experience working with entity models or structured data relationships (e.g., knowledge graphs, taxonomies, or similar systems) and how they help with search, discovery, or interpretation of information.

Nice to Have

Experience with graph databases

Experience in NLP, entity recognition, or data linking.

Familiarity with data quality, enrichment, or ETL workflows.

Background in semantic modeling or data unification.

Exposure to media intelligence, social listening, or marketing analytics environments.