TP, a global leader in human+AI business services, is building data products that power its operations, its clients’ services and its AI ambitions. As Data Modeller you will design and maintain the logical and physical data models behind TP’s data products, bringing data from many systems, countries and clients into one common, well-structured model and defining how each KPI is calculated. You will map each new source to this model and guide the engineers who implement it, using AI-assisted tools to make every new source faster than the last. Your models are the bridge between the business teams that define the data and the engineers and developers who build on it. It is a rare chance to design the data model every product is built on, at global scale and with direct visibility to TP’s leadership.
The Responsibilities and Duties
Success in this role requires rigor in structure and speed in repetition. Your primary responsibilities will include:
Designing how data fits together
Data Models: Design conceptual, logical and physical data models for each data product with the business owners and analysts, defining entities, relationships, grain, keys and history.
KPI Design: Design the gold layer so that every KPI has one formula and one place to be read, based on the business definitions agreed with the data stewards.
Shared Entities: Identify the key entities and data elements shared across TP’s products, such as employees, clients and sites, and model them once for reuse.
Designing how data fits together
Source Mapping: Map each new source to the model, country by country and client by client, with clear specifications and standardization rules for the data engineers.
Gap Analysis & Reuse: Assess each new source against the standard model, reuse what already fits and turn each solution into a template that makes the next source faster, supported by AI-assisted tools.
Implementation Support: Work with the data engineers to implement and optimize schemas, tables and relationships, and decide on model changes requested by the regions.
Keeping models reliable
Standards & Versioning: Apply modeling standards and naming conventions, version every model change and guide engineering and analytics teams on best practice.
Documentation & Metadata: Maintain data dictionaries, metadata, classification and lineage, with the governance team, so that everyone at TP can find and understand the data.
Model Review: Review each model version against the standards before release and close the findings.
The First-Year Objectives
Agree the Core Model: Get the business owners’ approval of the conceptual and logical models of the first data products.
Define the KPIs: Give every KPI in scope one formula in the gold layer, based on the business definitions agreed with the data stewards and consistent with the existing global reporting.
Map the First Sources: Complete the mapping specifications for the first countries and sources, accepted by the data engineers.
Take In the Regional Models: Compare the models of the first regional teams with the standard model, confirm the mapping with each team and promote their better definitions into the standard.
Speed Up New Sources: Publish mapping and gap-analysis templates so that each new country or client needs a fraction of the first one’s effort.
Document and Review: Give every column of the models in scope a definition, a source mapping and a classification, and pass the model review against the standards.
Requirements
The Qualifications
Domain Expertise
Education: Background in Computer Science, Information Systems, Engineering or Data Management; DAMA CDMP (Data Modeling and Design) is an advantage.
Professional Background: 5+ years of conceptual, logical and physical data modeling on large data warehouse or lakehouse platforms, including source-to-target mapping.
Modeling Methods: Strong command of entity-relationship, 3NF and dimensional modeling, model versioning and data history; awareness of data vault and semantic layers; advanced SQL and query optimization.
Tools & Platforms: Modeling tools such as erwin or ER/Studio, and modern cloud data platforms such as Databricks or Snowflake.
AI & Semantics: Understanding of semantic layers, metadata and knowledge graphs, and of how AI-assisted tools speed up mapping and gap analysis.
Proven Scale: Experience modeling many different sources and countries into one common model, under data protection rules such as GDPR.
Individual Skills
Business Analysis: Able to run workshops with business owners and turn requirements and KPIs into precise definitions and models.
Communication: Able to explain complex data structures to both technical and non-technical audiences.
Analytical Rigor: Strong analytical and problem-solving skills, with close attention to detail.
Languages: Fluent English; Spanish or Portuguese is an advantage.
Mindset & Behaviors
Designs for scalability, integrity and performance.
Takes pride in clean, well-documented models.
Bridges business needs and technical data structures.
TP’s Core Skills
Process Excellence: Champions process excellence, continuously driving enhanced efficiency, effectiveness, and quality across the entire organization.
AI Proficiency: Champions the integration of AI concepts and technologies across the organization to enhance organizational efficiency and innovation.
Data Literacy: Promotes a culture of data-driven leadership throughout the organization and creates the necessary environment for people to leverage data for informed decision-making.
Collaboration: Inspires collaboration by leveraging shared ideas and resources to drive the organization toward achieving common goals.
Communication: Communicates complex and strategic information to diverse stakeholder groups using various channels.
Emotional Intelligence: Shapes a culture of empathy and trust, creating an environment where positive relationships amplify the impact of actions.
Open-Mindedness: Shapes an environment of open-mindedness that encourages diverse perspectives, ensuring all voices are heard and considered.
Critical Thinking: Evaluates complex information and arguments leveraging data and insights, promoting a culture of critical thinking throughout the organization.
Solution Orientation: Creates an environment in which people focus on finding practical and effective solutions and ensures that those are realized.
Entrepreneurship: Cultivates an entrepreneurial mindset that emphasizes creativity, calculated risk-taking, proactive problem-solving, and opportunity identification.
Benefits
Competitive Compensation: A highly competitive salary and performance-based bonus structure aligned with your experience and market standards.
Global Environment: The opportunity to work in a dynamic, international, and multicultural environment with cross-border teams and global projects.
Career Growth: Clear pathways for professional development, internal mobility, and long-term career progression within a rapidly growing global company.
Cutting-Edge Technology: Work with the latest tools, platforms, and AI-driven technologies in the industry.
Diversity, Equity & Inclusion
*TP is home to a global family with various backgrounds and lifestyles. We will always embrace diversity and never discriminate against employees or applicants based on gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or other differences.*
Analytics
Communication
English
Spanish
Portuguese
Bonus
Pay
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