Hire Data Modeling Experts
Discover Data Modeling Experts from LatAm with Lupa. Skilled in dimensional modeling and architecture design with remote onboarding in just 21 days.
Hire Remote Data Modeling Experts
Luciana is a data expert helping teams unlock value through structured information.
- Analytics Tools
- ETL Processes
- Dashboard Creation
- Data Lifecycle Management
- Data Accuracy
Ana is a Data specialist who simplifies complex data into digestible business insights.
- Data Analysis
- Data Cleaning
- Reporting
- SQL
- Business Intelligence
Kevin is a data analyst translating numbers into meaningful business takeaways.
- Data Visualization
- SQL Analysis
- Trend Monitoring
- Insight Reporting
- Forecast Modeling
Lucas is a data operations expert improving systems through structured automation.
- Workflow Automation
- Data Governance
- Database Management
- Process Optimization
- ETL Engineering
Emilia, a skilled data scientist, excels at transforming complex data into actionable insights.
- Feature Engineering
- Statistics
- Data Cleaning
- Machine Learning
- Python
Camila is a data expert organizing models that enable smarter team decisions.
- Data Modeling
- Insight Reports
- Data Tool Integration
- Data Strategy
- Trend Analysis
Gabriela is a Data professional translating analytics into business opportunities.
- Data Analysis
- Business Intelligence
- SQL
- Reporting
- Data Visualization
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
Lupa's Proven Process
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Reviews
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”
"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"
“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”
"Highly professional, patient with our changes, and always maintaining clear communication with candidates. We look forward to continuing to work with you on all our future roles."
“Lupa has been an exceptional partner this year, deeply committed to understanding our unique needs and staying flexible to support us. We're excited to continue our collaboration into 2025.”
"What I love about Lupa is their approach to sharing small, carefully selected batches of candidates. They focus on sending only the three most qualified individuals, which has already helped us successfully fill 7 roles.”
"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."
"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."
"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."
“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”
Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!
Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.
Data Modeling Experts Soft Skills
Structured Thinking
Organize systems clearly with logical data design.
Collaboration
Work with BI, dev, and data teams on model usage.
Documentation
Maintain clarity in schema, lineage, and relationships.
Precision
Model data that supports performance and accuracy.
Problem Solving
Redesign structures to support scaling or changes.
Communication
Explain data logic to technical and non-technical teams.
Data Modeling Experts Skills
Dimensional Modeling
Design star and snowflake schemas for analytics.
Normalization & Denormalization
Structure relational models for flexibility or speed.
Data Architecture
Create scalable models aligned with business logic.
Tool Proficiency
Work in dbt, ER/Studio, or enterprise modeling tools.
Data Lineage Mapping
Track data flows from source to report.
BI Integration
Model data for easy use in Tableau, Looker, or Power BI.
How to Write an Effective Job Post to Hire Data Modeling Experts
Recommended Titles
- Data Modeler
- Data Architect
- Information Model Designer
- Database Schema Designer
- Conceptual Data Modeler
- Enterprise Data Architect
Role Overview
- Tech Stack: Experienced with ERwin, dbt, SQL, and cloud data platforms.
- Project Scope: Design scalable data models that support analytics, governance, and BI tooling.
- Team Size: Partner with analysts, engineers, and architects in cross-functional teams.
Role Requirements
- Years of Experience: At least 4 years modeling enterprise datasets or schemas.
- Core Skills: Dimensional modeling, normalization, data warehouse design, metadata definition.
- Must-Have Technologies: SQL, ERwin, dbt, Redshift, BigQuery.
Role Benefits
- Salary Range: $100,000 – $155,000 depending on modeling scale and industry domain.
- Remote Options: Remote-first with optional in-person workshops.
- Growth Opportunities: Define semantic layers and impact long-term data usability.
Do
- Include experience designing relational and dimensional models
- Mention ERDs, normalization, and schema design expertise
- List proficiency with data warehousing platforms
- Highlight alignment with BI and reporting requirements
- Use structured, database-aware job descriptions
Don't
- Don’t lump into analyst or warehouse roles without modeling clarity
- Avoid omitting ERD, normalization, and schema design
- Don’t use generic data terms without logical structure insight
- Refrain from listing only tools without techniques
- Don’t ignore business-layer alignment of data models
Top Data Modeling Expert Interview Questions
What to ask Data Modeling Experts in screening calls
How do you approach data modeling for analytical workloads?
Look for star/snowflake schema discussions, denormalization, and support for OLAP use cases.
What’s your experience with dimensional modeling?
Expect clear understanding of facts, dimensions, slowly changing dimensions, and surrogate keys.
How do you collaborate with stakeholders in modeling?
Strong answers include data discovery sessions, iterative prototyping, and business glossary alignment.
Which modeling tools or platforms have you used?
Expect dbt, ER/Studio, SAP PowerDesigner, or native tools in Redshift, BigQuery, or Snowflake.
How do you handle model refactoring as business evolves?
Look for incremental updates, versioning, backward compatibility, and stakeholder communication.
How do you resolve conflicts between dimensional and transactional models?
Look for hybrid modeling approaches, OLAP/OLTP separation, and business context clarification.
Describe a case where a model needed redesign due to performance issues.
Expect identification of joins, indexing problems, and structural re-architecture.
How do you validate the effectiveness of a data model?
Expect data accuracy tests, query performance benchmarks, and stakeholder feedback alignment.
What’s your process when a model doesn’t reflect business rules?
Expect collaborative workshops, model versioning, and documentation updates.
How do you balance normalization and query performance?
Expect understanding of denormalization trade-offs, indexing, and workload-aware design.
Tell me about a time you redesigned a data model for scalability.
Expect justification of choices, impact metrics, and collaboration across engineering teams.
How do you handle stakeholder pushback on model complexity?
Expect examples of simplification, clear communication, and compromise without sacrificing integrity.
Describe a project where you introduced new modeling standards.
Expect emphasis on documentation, training, and peer alignment.
How do you balance flexibility with enforceability in your data designs?
Expect structured governance, modularity, and iteration planning.
What’s your approach when inherited data models are poorly structured?
Expect reverse engineering, technical debt tracking, and phased improvements.
- Over-normalized or under-structured data models
- Inability to explain trade-offs between model types
- Fails to align models with analytical needs
- Poor documentation of data relationships
- Inflexible modeling not suited for scaling
Build elite teams in record time, full setup in 21 days or less.
Book a Free ConsultationWhy We Stand Out From Other Recruiting Firms
From search to hire, our process is designed to secure the perfect talent for your team
Local Expertise
Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.
Direct Control
Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.
Seamless Compliance
We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.
Lupa will help you hire top talent in Latin America.
Book a Free Consultation
Top Data Modeling Expert Interview Questions
What to ask Data Modeling Experts in screening calls
How do you approach data modeling for analytical workloads?
Look for star/snowflake schema discussions, denormalization, and support for OLAP use cases.
What’s your experience with dimensional modeling?
Expect clear understanding of facts, dimensions, slowly changing dimensions, and surrogate keys.
How do you collaborate with stakeholders in modeling?
Strong answers include data discovery sessions, iterative prototyping, and business glossary alignment.
Which modeling tools or platforms have you used?
Expect dbt, ER/Studio, SAP PowerDesigner, or native tools in Redshift, BigQuery, or Snowflake.
How do you handle model refactoring as business evolves?
Look for incremental updates, versioning, backward compatibility, and stakeholder communication.
How do you resolve conflicts between dimensional and transactional models?
Look for hybrid modeling approaches, OLAP/OLTP separation, and business context clarification.
Describe a case where a model needed redesign due to performance issues.
Expect identification of joins, indexing problems, and structural re-architecture.
How do you validate the effectiveness of a data model?
Expect data accuracy tests, query performance benchmarks, and stakeholder feedback alignment.
What’s your process when a model doesn’t reflect business rules?
Expect collaborative workshops, model versioning, and documentation updates.
How do you balance normalization and query performance?
Expect understanding of denormalization trade-offs, indexing, and workload-aware design.
Tell me about a time you redesigned a data model for scalability.
Expect justification of choices, impact metrics, and collaboration across engineering teams.
How do you handle stakeholder pushback on model complexity?
Expect examples of simplification, clear communication, and compromise without sacrificing integrity.
Describe a project where you introduced new modeling standards.
Expect emphasis on documentation, training, and peer alignment.
How do you balance flexibility with enforceability in your data designs?
Expect structured governance, modularity, and iteration planning.
What’s your approach when inherited data models are poorly structured?
Expect reverse engineering, technical debt tracking, and phased improvements.
- Over-normalized or under-structured data models
- Inability to explain trade-offs between model types
- Fails to align models with analytical needs
- Poor documentation of data relationships
- Inflexible modeling not suited for scaling
