Hire Big Data Engineers
Access Big Data Engineers from LatAm with Lupa. Experts in Spark, Kafka, and distributed systems onboarding fast for remote delivery in 21 days.
Hire Remote Big Data Engineers
Henrique is a data expert creating models that guide clear and confident decisions.
- SQL
- Data Modeling
- BI Reporting
- Forecasting
- Analytics Tools
Tomás is a skilled data analyst with a decade of experience, excelling in insightful analysis.
- Excel
- Data Visualization
- Power BI
- A/B Testing
- SQL
Bruna is a data expert turning analytical complexity into clear and simple takeaways.
- Data Modeling
- Analytics Tools
- Business Intelligence
- SQL
- Data Cleaning
Arturo is a data expert turning complex datasets into clear, actionable intelligence.
- Data Cleansing
- Analytics Tools
- Dashboarding
- Excel & SQL
- Data Storytelling
Angélica is a data interpreter simplifying complex metrics into clear insights.
- Data Communication
- Trend Interpretation
- Excel Proficiency
- Data Modeling
- Decision Support
"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”
Big Data Engineers Skills
Distributed Computing
Build data pipelines using Spark, Flink, or Hadoop.
Real-Time Processing
Stream data using Kafka, Flink, or AWS Kinesis.
Data Lake Architecture
Design storage solutions using S3, Delta Lake, or HDFS.
ETL & ELT Workflows
Build batch and real-time data ingestion pipelines.
Data Quality Monitoring
Implement checks to validate and clean incoming data.
Workflow Orchestration
Use Airflow or Prefect to manage data jobs and retries.
Big Data Engineers Soft Skills
Analytical Thinking
Approach large-scale data problems with clarity.
Resilience
Work through outages, bugs, and scaling issues.
Cross-Team Collaboration
Align with data scientists, analysts, and devs.
Communication
Explain complex systems in accessible terms.
Proactive Attitude
Identify and fix inefficiencies before they scale.
Documentation
Keep systems traceable and easy to maintain.
How to Hire Big Data Engineers with Lupa
Process large-scale datasets with expert Big Data Engineers from LatAm. Use our IT Recruiting Agency in Latin America, expand teams via Remote Staffing Agency, or hire long-term 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 Big Data Engineers
Recommended Titles
- Data Engineer
- Big Data Architect
- Hadoop Developer
- Spark Engineer
- Distributed Systems Engineer
- ETL Pipeline Developer
Role Overview
- Tech Stack: Experienced in Spark, Hadoop, Kafka, Scala, and Python.
- Project Scope: Design and optimize large-scale ETL pipelines and data lakes.
- Team Size: Join a data engineering unit of 5–7 focused on streaming and batch processing.
Role Requirements
- Years of Experience: At least 3 years in distributed data systems engineering.
- Core Skills: Parallel processing, data partitioning, schema design, and stream ingestion.
- Must-Have Technologies: Spark, Hadoop, Kafka, Hive, Airflow, SQL.
Role Benefits
- Salary Range: $105,000 – $170,000 based on system architecture skills and domain knowledge.
- Remote Options: Remote-friendly with distributed team support across regions.
- Growth Opportunities: Work on mission-critical data infrastructure and real-time systems.
Do
- List Hadoop, Spark, Kafka, and distributed systems expertise
- Mention experience in ETL pipeline development at scale
- Highlight cross-functional work with data science teams
- Include focus on real-time or batch processing systems
- Use high-performance, data infrastructure terminology
Don't
- Don’t conflate with generic backend or ETL roles
- Avoid listing tools like Hadoop/Spark without project relevance
- Don’t ignore data volume or pipeline performance specifics
- Refrain from using outdated tech without modern context
- Don’t exclude real-time or distributed architecture experience
Top Big Data Engineer Interview Questions
How to evaluate Big Data Engineer proficiency
What big data tools and platforms are you most familiar with?
Expect Hadoop, Spark, Kafka, Hive, or cloud-native tools. Look for depth in pipeline architecture.
How do you ensure fault tolerance in distributed data systems?
Look for replication strategies, retry mechanisms, and checkpointing in tools like Spark or Kafka.
Can you describe a data pipeline you’ve built end-to-end?
They should explain ingestion, transformation, storage, orchestration, and monitoring components.
How do you optimize performance in data-intensive systems?
Expect use of partitioning, caching, lazy evaluation, or parallelism to manage compute cost and latency.
What’s your approach to data governance and security?
Strong answers include encryption, data lineage, access control, and compliance with GDPR or HIPAA if applicable.
How do you identify performance bottlenecks in distributed data processing?
Expect profiling with Spark UI, skewed data handling, and tuning of partitioning strategies.
Describe a time you fixed a failure in a data pipeline under load.
They should walk through log inspection, rollback or replay techniques, and fault-tolerant system design.
How do you handle schema evolution in a big data architecture?
Expect experience with Avro/Parquet, backward compatibility strategies, and metadata management.
How do you debug inconsistent results across data nodes?
Look for data validation steps, cluster diagnostics, and replication logic debugging.
What’s your approach when batch jobs fail randomly?
Expect inspection of dependency failures, resource contention, retry logic, and idempotency strategies.
Tell me about a time you optimized a data pipeline for scale.
Expect examples of architecture changes, streaming vs. batch decisions, and cost trade-offs.
Describe how you handle conflicting data requirements from multiple teams.
Expect collaboration, data governance awareness, and prioritization logic.
How do you ensure resilience in distributed data systems?
Look for failover strategies, monitoring setup, and experience handling outages.
What’s your approach when a production data job silently fails?
Expect proactive alerting, verification protocols, and rollback planning.
Describe a time your work uncovered critical business insight.
Expect storytelling, cross-team collaboration, and impact awareness.
- Ignores data governance and pipeline observability
- Lack of experience with distributed system bottlenecks
- Weak schema design for scalable processing
- Fails to monitor data flow and latency effectively
- Overcomplicates data pipelines without maintainability
Build elite teams in record time, full setup in 21 days or less.
Book a discovery callLatAm Talent: A Smart Recruiting Solution
High-Performing Talent, Cost-Effective Rates
Top LatAm tech professionals at up to 80% lower rates — premium skills, unbeatable savings
Zero Time Zone Barriers, Efficient Collaboration
Aligned time zones enable seamless collaboration, efficiency and faster project deliveries
Vibrant Tech Culture, World-Class Tech Skills
World-class training and a dynamic tech scene fuel LatAm’s exceptional talent pool
Our All-in-One Hiring Solutions
End-to-end remote talent solutions, from recruitment to payroll. Country-compliant throughout LatAm.
Recruiting
Our recruiting team delivers pre-vetted candidates within a week. Not the perfect match? We iterate until you're satisfied. You control hiring and contracts, while we provide guidance.
Staffing
Our recruiters deliver pre-vetted remote talent in a week. You select the perfect candidate, we manage onboarding, contracts, and ongoing payroll seamlessly.
RPO
Our RPO services deliver flexible talent solutions. From targeted support to full-cycle recruitment, we adapt and scale to meet your hiring goals while you focus on strategic growth.
