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
Natalia excels in data analysis, delivering insights with precision and creativity.
- Power BI
- Data Visualization
- A/B Testing
- Excel
- SQL
Jimena is a data strategist turning patterns into actionable business direction.
- Data Mapping
- Business Intelligence
- Forecasting Models
- Metric Analysis
- Visualization Tools
Caio is a data expert simplifying analytics to deliver insights that support decisions.
- Data Modeling
- SQL
- Data Visualization
- ETL Processes
- Analytics Reporting
María José is a data specialist focused on delivering insights that support decision-making.
- Data Analysis
- Reporting
- Business Intelligence
- Data Modeling
- Data Visualization
Esteban is a data thinker turning strategy into action with practical insights.
- Data Exploration
- BI Dashboards
- Statistical Modeling
- Data Storytelling
- Insight Generation
"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”
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.
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.
How to Hire Data Warehouse Developers with Lupa
Build secure, scalable warehouse solutions with expert developers. Use our Tech Recruiting Agency, grow flexibly with Remote Staffing Services, or scale hiring through our RPO support.
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.
Top candidates ready for your assessment. We handle interview logistics and feedback collection—ensuring smooth evaluation. Not fully convinced? We iterate until you find the perfect fit.
We manage contracting, onboarding, and payment to your team seamlessly. Our partnership extends beyond hiring—providing retention support and strategic guidance for the long-term growth of your LatAm team.
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
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