Hire Data Engineers

Hire Data Engineers

Hire senior data engineers from Latin America in 8-12 days. Build scalable data pipelines, warehouses, and infrastructure that power analytics, ML, and business intelligence.

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TL;DR

Hire data engineers in 8-12 days: Get vetted, senior-level data engineers from Latin America who build scalable data pipelines, modern data warehouses, and infrastructure that powers analytics and ML. Work in US time zones, cost 40-60% less than domestic hires. Meet qualified candidates in 48 hours.

Key Benefits:

  • Fast Placement: Meet candidates in 48 hours, fully onboarded in 8-12 days
  • Senior Expertise: 5+ years experience, Python/SQL, Airflow, dbt, Snowflake/BigQuery, Spark
  • Modern Data Stack: dbt, Fivetran, Snowflake, Airflow, not legacy ETL tools
  • Cost Effective: $5,500-9,000/month vs $13,000-17,000+ for US data engineers
  • Time Zone Aligned: 0-3 hour difference for real-time collaboration
  • Retention Guarantee: Free replacement within 90 days
  • Full Support: Payroll, benefits, HR, equipment, team retention

Why Hire Data Engineers Through Ideaware?

Finding data engineers who build modern, scalable data infrastructure—not just SQL queries and Excel exports—is challenging. You need engineers who understand data modeling, orchestration, data quality, performance optimization, and the modern data stack (dbt, Fivetran, Snowflake, Airflow).

At Ideaware, we’ve vetted thousands of developers across Latin America. We maintain a pipeline of 200+ data engineers ready to interview this week.

We Actually Vet for Data Engineering Expertise: Not just SQL tutorials. We test for data pipeline design, dbt modeling, orchestration (Airflow/Prefect), warehouse optimization, data quality frameworks, and production data infrastructure.

We Move Fast: 48 hours to candidates. 8-12 days to onboarded engineers.

We Handle Everything: Payroll, benefits, equipment, HR, retention, career development.

We Guarantee Results: Free replacement if it doesn’t work out. 2x industry average retention rate.

What Our Data Engineers Do

  • Build data pipelines using Python, SQL, Airflow, Prefect for ETL/ELT workflows that move data from sources to warehouses
  • Design data warehouses with dimensional modeling, star/snowflake schemas, and optimized table structures for analytics
  • Implement dbt models for data transformation, testing, documentation, and version-controlled analytics engineering
  • Orchestrate data workflows with Airflow, Prefect, or Dagster for scheduling, dependency management, and monitoring
  • Integrate data sources using Fivetran, Airbyte, custom connectors for APIs, databases, SaaS tools, and file systems
  • Optimize query performance through indexing, partitioning, clustering, materialized views, and warehouse tuning
  • Ensure data quality with automated testing, validation rules, monitoring, and alerting for data issues
  • Build real-time pipelines using Kafka, Kinesis, or Pub/Sub for streaming data and event-driven architectures
  • Manage cloud data platforms on Snowflake, BigQuery, Redshift, Databricks with cost optimization and governance
  • Process big data with Spark, distributed computing for large-scale data transformation
  • Create data APIs to serve cleaned, aggregated data to applications, dashboards, and ML systems
  • Collaborate with analysts and data scientists to understand data needs and deliver reliable infrastructure

When to Hire Data Engineers

Business Intelligence: Building data infrastructure for analytics dashboards, reports, and executive decision-making

Data Warehousing: Centralizing data from multiple sources into a single source of truth

Analytics Engineering: Transforming raw data into clean, modeled datasets for analysts

ML Data Pipelines: Preparing and serving data for machine learning model training and inference

Real-Time Analytics: Streaming data pipelines for live dashboards, monitoring, fraud detection

Data Migration: Moving from legacy systems to modern cloud data platforms

Operational Analytics: Customer 360, product analytics, financial reporting, supply chain visibility

Common Tech Stack & Skills

Data Warehouses:

  • Snowflake
  • Google BigQuery
  • Amazon Redshift
  • Databricks
  • Azure Synapse

Orchestration:

  • Apache Airflow
  • Prefect
  • Dagster
  • Mage
  • AWS Step Functions

Transformation:

  • dbt (Data Build Tool)
  • SQL
  • Python (Pandas, PySpark)
  • Spark

Data Integration:

  • Fivetran
  • Airbyte
  • Stitch
  • Custom API connectors
  • Change Data Capture (CDC)

Streaming:

  • Apache Kafka
  • AWS Kinesis
  • Google Pub/Sub
  • Apache Flink
  • Spark Streaming

Programming:

  • Python (primary)
  • SQL (advanced)
  • Bash scripting
  • Git version control

Cloud Platforms:

  • AWS (S3, Redshift, Glue, Lambda, EMR)
  • GCP (BigQuery, Dataflow, Cloud Storage)
  • Azure (Synapse, Data Factory, Blob Storage)

Data Quality:

  • Great Expectations
  • dbt tests
  • Monte Carlo
  • Custom validation frameworks

Monitoring:

  • Datadog
  • Monte Carlo
  • Airflow monitoring
  • Custom alerting

Pricing & Engagement

Senior Data Engineers: $5,500-9,000/month

  • Full-time (40 hrs/week)
  • Benefits & payroll
  • HR support
  • Equipment
  • Retention programs

Compare to US data engineers: $150,000-200,000/year = $12,500-16,667/month

Your savings: 40-56% without sacrificing quality.

Frequently Asked Questions

How much does it cost to hire data engineers?

Senior data engineers cost $5,500-9,000/month including full-time employment, benefits, HR support, and retention programs. This is 40-56% less than US-based data engineers with no recruiting fees.

How quickly can I hire data engineers?

48 hours to vetted profiles. 8-12 days to fully onboarded. 10-15 weeks faster than traditional hiring.

What skills should data engineers have?

Essential: Python, SQL (advanced), data warehouse (Snowflake/BigQuery), dbt, orchestration (Airflow/Prefect), ETL/ELT design, data modeling, cloud platforms (AWS/GCP), data quality, performance optimization. Bonus: Spark, Kafka, real-time streaming, infrastructure-as-code.

Are nearshore data engineers as good as US developers?

Yes. Many hold CS or data-related degrees, 70%+ have US company experience, work in US time zones with strong English. Data engineering best practices are universal. 80% hire rate, 2x retention rate.

What is your vetting process for data engineers?

Only 3% pass: (1) Data engineering technical assessment (SQL, Python, pipeline design), (2) 90-min live coding building a data pipeline, (3) data warehouse architecture review, (4) portfolio review of production data systems, (5) cultural fit evaluation, (6) reference checks. We test practical data engineering, not just SQL queries.

What time zones do data engineers work in?

0-3 hour difference from US time zones. Real-time collaboration during business hours for pipeline debugging, data modeling discussions, and sprint planning.

Can I hire a full data engineering team?

Yes. Solo data engineers, pairs, or complete data teams (2-3 data engineers + analytics engineers + data analysts + data architect). Scale as needed.

What if it doesn't work out?

Free replacement within 90 days. No fees or penalties.

Data Engineers vs Data Scientists vs Analytics Engineers: What's the difference?

Data Engineers focus on building data infrastructure—pipelines, warehouses, orchestration, data quality, integration. They ensure data is reliable, accessible, and scalable. Software engineers who specialize in data systems.

Data Scientists focus on insights and modeling—statistical analysis, machine learning, predictive models, business insights. They consume data prepared by data engineers.

Analytics Engineers focus on transforming data for analytics—dbt models, data modeling, metrics definitions, BI layer. Bridge between data engineers and analysts. Often use SQL and dbt more than Python.

When to hire Data Engineers:

  • Building data pipelines from scratch
  • Integrating multiple data sources
  • Data warehouse architecture and optimization
  • Real-time streaming data
  • Scaling data infrastructure
  • Data platform engineering

When to hire Data Scientists:

  • Predictive modeling and ML
  • Statistical analysis and insights
  • A/B testing and experimentation
  • Advanced analytics

When to hire Analytics Engineers:

  • dbt transformations and modeling
  • Business metrics and KPIs
  • BI tool integration (Looker, Tableau)
  • SQL-heavy data transformation

Our take: Data Engineers lay the foundation. Without solid data infrastructure, data scientists and analysts can’t be productive. Start with data engineers if you’re building from scratch or have data quality/scalability issues.

What ongoing support do you provide?

Full HR support (payroll, benefits), career development (data conferences, training), retention programs, operational support, strategic partnership (quarterly reviews, pipeline).

What are the contract terms?

Month-to-month with 30-day notice. No long-term commitments, no placement fees. Free 90-day replacement guarantee.

Who owns the data infrastructure and code?

You own 100%. All IP, code, data models, pipelines, and documentation assigned to your company with NDAs and secure protocols.

How good is the English communication?

Business-level required. Clients rate 4.7/5. Full participation in technical discussions without friction.

Do you provide junior or only senior data engineers?

Primarily senior (5+ years): $5,500-9,000/month. Also: mid-level (3-5 years): $4,500-6,500/month. Junior data engineers available but not recommended for critical data infrastructure.

Are your data engineers experienced with the modern data stack?

Yes. Our data engineers are trained in the modern data stack: dbt for transformation, Fivetran/Airbyte for ingestion, Snowflake/BigQuery for warehousing, Airflow/Prefect for orchestration, and cloud-native tools. We specifically filter for engineers who’ve moved beyond legacy ETL tools (Informatica, SSIS) to cloud-first, code-based approaches. Most have 2+ years experience with dbt and cloud data warehouses.

Next Steps

  1. Book Discovery Call - 30 minutes to discuss your data infrastructure needs
  2. Meet Candidates - 48 hours to receive vetted data engineer profiles
  3. Interview & Select - Week 1, you control the process
  4. Onboard & Ship - Week 2, engineer integrated into your team
  5. Scale as Needed - Add more data engineers or build complete data teams

Schedule your discovery call and meet qualified data engineers in 48 hours.