Hire Data Warehouse Developers
Connect with Data Warehouse Developers from Latin America. Experienced in schema design, ETL, and cloud storage with seamless setup in 21 days.
Hire Remote Data Warehouse Developers
Óscar is a data strategist who translates metrics into useful operational decisions.
- Data Analysis
- Analytics Strategy
- Dashboarding
- Data Storytelling
- BI Tools
Benjamín is a data scientist designing models that scale with clarity and precision.
- Big Data Analysis
- ETL Processes
- Data Warehousing
- Python & SQL
- Insight Generation
Noelia is a data expert structuring insights to support confident decision-making.
- Data Architecture
- SQL & Databases
- Analytics Reports
- Trend Forecasting
- Data Governance
Tiago is a data systems thinker building clarity into complex analytical models.
- Data Architecture
- ETL Processes
- Data Modeling
- Performance Metrics
- Automation Workflows
Emilia, a skilled data scientist, excels at transforming complex data into actionable insights.
- Feature Engineering
- Statistics
- Data Cleaning
- Machine Learning
- Python
Beatriz is a data-driven professional turning numbers into direction and clarity.
- Data Analysis
- Business Reporting
- SQL
- Insights Generation
- Dashboard Design
Kevin is a data analyst translating numbers into meaningful business takeaways.
- Data Visualization
- SQL Analysis
- Trend Monitoring
- Insight Reporting
- Forecast Modeling
"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 Warehouse Developers Soft Skills
Logical Thinking
Structure scalable systems for long-term growth.
Collaboration
Work with analysts and engineers on data delivery.
Accountability
Own performance and reliability of data pipelines.
Documentation
Keep architecture and transformations well documented.
Communication
Explain warehouse logic to business stakeholders.
Problem Solving
Diagnose and fix performance bottlenecks quickly.
Data Warehouse Developers Skills
Warehouse Architecture
Design scalable data layers using Redshift or BigQuery.
ETL Pipeline Development
Ingest, transform, and load data for reporting use.
Schema Design
Create normalized and denormalized warehouse schemas.
Data Partitioning
Improve performance with smart table partitioning.
Access Control
Set user permissions for secure data access.
Performance Optimization
Tune queries, indexes, and table structures.
How to Write an Effective Job Post to Hire Data Warehouse Developers
Recommended Titles
- Data Warehouse Engineer
- ETL Developer
- Database Engineer – DWH
- Snowflake Developer
- SQL Data Engineer
- BigQuery Developer
Role Overview
- Tech Stack: Skilled in SQL, Snowflake/BigQuery, ETL frameworks, and Python.
- Project Scope: Design, build, and optimize central data warehouses and reporting layers.
- Team Size: Join a data engineering team of 5–7 focused on scalable architectures.
Role Requirements
- Years of Experience: At least 3 years in data warehousing or ETL development.
- Core Skills: Data modeling, query optimization, automation, and pipeline orchestration.
- Must-Have Technologies: SQL, ETL tools, Snowflake/BigQuery, Airflow, Python.
Role Benefits
- Salary Range: $95,000 – $155,000 depending on scale and cloud expertise.
- Remote Options: Fully remote with timezone flexibility and async coordination.
- Growth Opportunities: Drive data strategy and influence analytics performance at scale.
Do
- Specify skills in building and optimizing data warehouses
- Mention platforms like Snowflake, Redshift, or BigQuery
- List ETL/ELT pipeline design and schema structuring
- Highlight collaboration with data analysts and engineers
- Use cloud-native and scale-focused language
Don't
- Don’t treat like generic database admin roles
- Avoid skipping mention of cloud platforms (Snowflake, BigQuery)
- Don’t list ETL tools without transformation logic clarity
- Refrain from omitting schema design or partitioning skills
- Don’t exclude performance optimization duties
Top Data Warehouse Developer Interview Questions
Interview questions for Data Warehouse Developer candidates
What data warehouse platforms have you worked with?
Expect Snowflake, Redshift, BigQuery, or Synapse. Bonus if they can compare trade-offs between them.
How do you structure data for analytical workloads?
Look for partitioning, clustering, and denormalization strategies aligned with reporting needs.
Describe your ETL/ELT process for loading warehouses.
Expect mention of orchestration tools like Airflow or dbt, plus data quality and monitoring practices.
How do you ensure query performance in large datasets?
Look for tuning, materialized views, caching, cost analysis, and indexing or metadata optimization.
What’s your approach to warehouse cost management?
They should talk about query optimization, warehouse sizing, auto-suspend policies, and usage monitoring.
How do you troubleshoot ETL failures during warehouse load?
Look for log reviews, staging table checks, and rollback or replay pipeline strategies.
Describe a time you fixed performance issues in large data queries.
Expect query plan inspection, index optimization, and materialized view usage.
How do you handle schema drift in a production data warehouse?
Expect version-controlled schema evolution, alerting on changes, and backward compatibility handling.
What’s your approach to maintaining data freshness and consistency?
Look for batch vs. stream decisions, job scheduling, and validation of deltas.
What do you do when reports show inconsistent metrics?
Expect data lineage tracing, metric definition audits, and transformation logic review.
Tell me about a time you fixed a performance bottleneck in a warehouse system.
Expect clear steps taken, impact metrics, and team collaboration.
How do you handle competing demands from multiple BI teams?
Expect structured intake processes, prioritization, and governance awareness.
Describe how you aligned business goals with your data models.
Expect use of KPIs, stakeholder interviews, and mapping to core business logic.
What do you do when your warehouse schema becomes too complex?
Expect refactoring approaches, documentation, and normalization strategies.
Have you managed warehouse migration? What worked?
Expect discussion of phased rollouts, testing, and rollback planning.
- Inadequate partitioning and indexing strategies
- Fails to manage slow-running queries effectively
- Weak understanding of ELT vs. ETL workflows
- Neglects schema evolution and versioning
- Overuse of nested queries or inefficient joins
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 Warehouse Developer Interview Questions
Interview questions for Data Warehouse Developer candidates
What data warehouse platforms have you worked with?
Expect Snowflake, Redshift, BigQuery, or Synapse. Bonus if they can compare trade-offs between them.
How do you structure data for analytical workloads?
Look for partitioning, clustering, and denormalization strategies aligned with reporting needs.
Describe your ETL/ELT process for loading warehouses.
Expect mention of orchestration tools like Airflow or dbt, plus data quality and monitoring practices.
How do you ensure query performance in large datasets?
Look for tuning, materialized views, caching, cost analysis, and indexing or metadata optimization.
What’s your approach to warehouse cost management?
They should talk about query optimization, warehouse sizing, auto-suspend policies, and usage monitoring.
How do you troubleshoot ETL failures during warehouse load?
Look for log reviews, staging table checks, and rollback or replay pipeline strategies.
Describe a time you fixed performance issues in large data queries.
Expect query plan inspection, index optimization, and materialized view usage.
How do you handle schema drift in a production data warehouse?
Expect version-controlled schema evolution, alerting on changes, and backward compatibility handling.
What’s your approach to maintaining data freshness and consistency?
Look for batch vs. stream decisions, job scheduling, and validation of deltas.
What do you do when reports show inconsistent metrics?
Expect data lineage tracing, metric definition audits, and transformation logic review.
Tell me about a time you fixed a performance bottleneck in a warehouse system.
Expect clear steps taken, impact metrics, and team collaboration.
How do you handle competing demands from multiple BI teams?
Expect structured intake processes, prioritization, and governance awareness.
Describe how you aligned business goals with your data models.
Expect use of KPIs, stakeholder interviews, and mapping to core business logic.
What do you do when your warehouse schema becomes too complex?
Expect refactoring approaches, documentation, and normalization strategies.
Have you managed warehouse migration? What worked?
Expect discussion of phased rollouts, testing, and rollback planning.
- Inadequate partitioning and indexing strategies
- Fails to manage slow-running queries effectively
- Weak understanding of ELT vs. ETL workflows
- Neglects schema evolution and versioning
- Overuse of nested queries or inefficient joins
