Hire the best Big Data developers in Latin America
400k+
ENGINEERS
14 days
to hire
100+
COVERED
30-50%
US hires
Why hire Big Data developers through Revelo?
Rigorously vetted senior developers from Latin America who work in your timezone, ready to contribute from day one.
Interview only the best Big Data developers
A shortlist of three to five pre-vetted candidates, hand-picked by in-market recruiters. You decide who to interview, you decide who to hire.
One platform for talent, payroll, taxes and compliance
Your team runs legally across 18 countries in Latin America. Manage your engineers without managing the infrastructure underneath them.
Local recruiting experts invested in your hire
In-market recruiters and account managers cover sourcing, offer strategy, and onboarding. They stay with you until your engineer is up and running.
Your team, your terms
Month-to-month engagements mean you're never locked into headcount you don't need. Scale up for a big push, pull back after launch.
Hire the top 1% of Big Data developers in Latin America
Hire vetted senior developers, matched to your stack, your timezone, and your budget.
Hire Big Data developers who can deliver this and more
Here's what you get when you hire nearshore Big Data developers with Revelo.
Hire Big Data developersRevelo's Big Data developers are placed across the full data engineering stack, from raw ingestion through warehouse modeling and pipeline reliability. Here's where they take ownership:
Data Pipeline Design and Build
Revelo's engineers architect and build ingestion and transformation pipelines using Spark, Kafka, Airflow, and dbt, handling both batch and real-time workloads at production scale.
Cloud Data Warehouse Implementation
They design and build out Snowflake, BigQuery, or Redshift environments from scratch, including schema modeling, access control, and the transformation layers that sit on top.
Streaming and Real-Time Analytics
For teams that need sub-minute latency on event data, Revelo's developers build and operate streaming architectures using Kafka, Flink, or Spark Streaming, with the operational tooling to keep them running reliably.
Data Quality and Observability
They instrument pipelines with data quality checks, alerting, and lineage tracking so your team knows when something breaks before a business stakeholder notices it first.
ML Feature Pipelines
For teams with machine learning in production or on the roadmap, Revelo's Big Data engineers build the feature engineering pipelines that feed model training and inference, including feature stores when the scale justifies it.
Hire Big Data developers in 4 simple steps
Get from "we need someone" to your first day together in weeks, not months.
Tell us what you're building and what kind of Big Data developers you need: skills, experience level, team dynamics.
Three to five matched, pre-vetted candidates: identity-checked, skills-tested, human-screened. No wading through hundreds of profiles.
Run your own technical interviews. You decide who to interview and who to hire. Full control, no gatekeeping.
Make the offer. Revelo handles payroll, benefits, taxes, and compliance so you can focus on building. Your engineer ships code from day one.
10+ years making it easier to hire elite nearshore Big Data developers
Interview pre-vetted candidates who are fluent in English and work in your timezone.
Start hiring
Why hire Big Data developers based in Latin America?
Work synchronously with Big Data developers in the same or overlapping US time zones. Real-time collaboration, no async tax.
What are Big Data developers?
A Big Data developer designs, builds, and maintains the systems that collect, store, process, and surface large-scale datasets, typically in the hundreds of gigabytes to petabyte range. They own the pipelines that move raw data from source systems into formats analysts and machine learning engineers can actually use.
Day to day, that means building ingestion layers with tools like Apache Kafka or Spark, modeling data warehouses in Snowflake or BigQuery, writing the transformation logic in dbt or PySpark, and keeping the whole stack reliable at scale. Strong Big Data developers think in tradeoffs: batch versus streaming, normalized versus denormalized, consistency versus availability.
What separates a strong candidate from an average one is systems judgment. Anyone can write a Spark job. The engineers worth hiring know when Spark is the wrong tool, how to tune a cluster that's hemorrhaging money, and how to architect a pipeline that survives a 10x data spike without a 3 a.m. page.
Why hire Big Data developers?
Big Data developers turn raw event logs and fragmented source systems into the reporting, ML features, and product analytics your business runs on. Without this layer, your data scientists are cleaning CSVs by hand and your product team is making roadmap calls from gut feel.
The role is genuinely hard to fill in the US. Big Data engineering sits at the intersection of distributed systems, cloud infrastructure, and data modeling, and the US talent pool that covers all three deeply is thin. Companies like Google, Amazon, Meta, Anthropic, and OpenAI sweep up most of it at total comp packages that mid-market companies can't match.
Revelo solves this directly. Its network covers 400,000+ pre-vetted engineers across Latin America, with deep concentration in data and backend engineering. Clients get a shortlist in 72 hours and hire in 14 days on average, at 30–50% less than comparable US hiring, with full timezone overlap for live collaboration.
What does it cost to hire Big Data developers?
In the US, senior software developers earn roughly $140,000 to $220,000 per year in base salary, according to Levels.fyi compensation data. Big Data specialists with Spark, Kafka, or cloud warehouse depth typically sit toward the upper end of that band, putting fully loaded annual cost (salary plus benefits, payroll taxes, and recruiter fees) well above $250,000 for a single senior hire.
Engineers based in Latin America price materially lower. The all-in cost through Revelo, which includes engineer compensation, compliance framework, benefits administration, PTO, and holidays, runs $86,000–$125,000 per year for senior-level Big Data hires, consistent with Revelo's published Salary Guide anchors for senior backend and data roles, with Big Data specialists with strong Spark or streaming depth typically toward the higher end of that band.
Junior roles start around $56,000–$67,000 all-in; mid-level pricing sits between the junior and senior anchors above. For a current, role-specific figure, use the pricing calculator at revelo.com/pricing. Revelo's pricing is published and transparent, with no large upfront fee and no long-term contract.
| Seniority | LatAm All-In Cost (Revelo) |
|---|---|
| Junior | ~$56,000 – $67,000 |
| Mid-Level | Between junior and senior anchors above |
| Senior | $86,000 – $125,000 |
Note: All-in cost includes engineer compensation, compliance framework, benefits administration, and Revelo's platform fee. Based on Revelo Salary Guide 2024–2026. Use revelo.com/pricing for a role-specific quote.
Why hire in Latin America?
Latin America has built real depth in data and backend engineering over the past decade. Brazil's tech sector, anchored in São Paulo and Florianópolis, has produced a generation of engineers who came up on large-scale data problems at banks, fintechs, and e-commerce platforms processing millions of daily transactions. Colombia, Argentina, and Mexico have followed with strong university programs and a growing base of engineers with cloud and streaming experience.
The timezone argument is concrete for Big Data work specifically. Pipeline incidents, schema migrations, and warehouse outages need real-time collaboration. Engineers in Bogotá, Mexico City, Buenos Aires, and São Paulo work within 0 to 2 hours of US Eastern for most of the year, which means your on-call rotation, your data team standups, and your production incidents all happen during business hours.
English fluency is a standard part of Revelo's screening, and engineers who pass it are evaluated specifically on their ability to explain technical decisions to a non-specialist audience, the same skill that matters when a Big Data developer has to walk a product manager through a pipeline outage. Cultural alignment with US engineering practices, including code review norms, sprint cadences, and documentation habits, is high across the region.
How to evaluate Big Data developers
Start by probing systems design judgment. Ask a candidate to walk you through a pipeline they built from scratch: what the ingestion layer looked like, how they handled late-arriving data, and what they'd change if they built it again. A strong answer names specific tradeoffs and owns the decisions; a weak one describes the happy path without acknowledging failure modes.
Second, test distributed systems depth. Ask how they'd tune a Spark job that's spilling to disk on a cluster that should have enough memory. Weak candidates reach for "add more nodes." Strong candidates ask about the data distribution first, suspect skew, and walk through join strategies before touching cluster config.
Third, assess operational maturity. Ask how they monitor a streaming pipeline for data quality drift. A strong candidate has opinions on alerting thresholds, dead-letter queues, and schema evolution. Someone who has only built pipelines and never run them in production will give you a vague answer about dashboards.
Why expertise matters
Why Big Data Wins for Scale and Throughput
Big Data tooling, particularly the Spark and Kafka stack, is purpose-built for workloads that relational databases choke on. When you're processing billions of events a day, running multi-hour batch transforms across terabyte-scale tables, or serving ML features at low latency across distributed systems, the architecture gives you horizontal scale that a single Postgres instance can't provide. The tradeoff is operational complexity; the payoff is throughput that scales with your data volume.
Common Use Cases
Product analytics at scale, real-time fraud detection, recommendation system feature pipelines, log aggregation and anomaly detection, financial reporting across high-transaction systems, and ETL from fragmented SaaS data sources into a centralized warehouse.
Companies Shipping Big Data in Production
Uber built its entire ride pricing and supply-demand model on a Kafka and Flink streaming backbone. LinkedIn open-sourced Kafka precisely because they needed it for their own feed and analytics infrastructure. Netflix runs Spark at petabyte scale for content recommendation. Airbnb built Airflow internally to manage the complexity of its data pipeline dependencies before open-sourcing it.
When Big Data Is the Wrong Choice
If your dataset fits comfortably in memory, or your team is processing a few million rows per day, you don't need a distributed compute layer. Spark has real overhead in cluster management and debugging complexity; DuckDB, Postgres, or a simple dbt project on a managed warehouse will ship faster and cost less to operate. Reach for Big Data tooling when the scale genuinely demands it, not because the architecture looks impressive on a diagram.
Benefits of working with Big Data developers
Revelo's Big Data developers are placed across the full data engineering stack, from raw ingestion through warehouse modeling and pipeline reliability. Here's where they take ownership:
Data Pipeline Design and Build
Revelo's engineers architect and build ingestion and transformation pipelines using Spark, Kafka, Airflow, and dbt, handling both batch and real-time workloads at production scale.
Cloud Data Warehouse Implementation
They design and build out Snowflake, BigQuery, or Redshift environments from scratch, including schema modeling, access control, and the transformation layers that sit on top.
Streaming and Real-Time Analytics
For teams that need sub-minute latency on event data, Revelo's developers build and operate streaming architectures using Kafka, Flink, or Spark Streaming, with the operational tooling to keep them running reliably.
Data Quality and Observability
They instrument pipelines with data quality checks, alerting, and lineage tracking so your team knows when something breaks before a business stakeholder notices it first.
ML Feature Pipelines
For teams with machine learning in production or on the roadmap, Revelo's Big Data engineers build the feature engineering pipelines that feed model training and inference, including feature stores when the scale justifies it.
What Is a Big Data Developer?
A Big Data developer designs, builds, and maintains the systems that collect, store, process, and surface large-scale datasets, typically in the hundreds of gigabytes to petabyte range. They own the pipelines that move raw data from source systems into formats analysts and machine learning engineers can actually use.
Day to day, that means building ingestion layers with tools like Apache Kafka or Spark, modeling data warehouses in Snowflake or BigQuery, writing the transformation logic in dbt or PySpark, and keeping the whole stack reliable at scale. Strong Big Data developers think in tradeoffs: batch versus streaming, normalized versus denormalized, consistency versus availability.
What separates a strong candidate from an average one is systems judgment. Anyone can write a Spark job. The engineers worth hiring know when Spark is the wrong tool, how to tune a cluster that's hemorrhaging money, and how to architect a pipeline that survives a 10x data spike without a 3 a.m. page.
Why Hire Big Data Developers?
Big Data developers turn raw event logs and fragmented source systems into the reporting, ML features, and product analytics your business runs on. Without this layer, your data scientists are cleaning CSVs by hand and your product team is making roadmap calls from gut feel.
The role is genuinely hard to fill in the US. Big Data engineering sits at the intersection of distributed systems, cloud infrastructure, and data modeling, and the US talent pool that covers all three deeply is thin. Companies like Google, Amazon, Meta, Anthropic, and OpenAI sweep up most of it at total comp packages that mid-market companies can't match.
Revelo solves this directly. Its network covers 400,000+ pre-vetted engineers across Latin America, with deep concentration in data and backend engineering. Clients get a shortlist in 72 hours and hire in 14 days on average, at 30–50% less than comparable US hiring, with full timezone overlap for live collaboration.
What Does It Cost to Hire a Big Data Developer?
In the US, senior software developers earn roughly $140,000 to $220,000 per year in base salary, according to Levels.fyi compensation data. Big Data specialists with Spark, Kafka, or cloud warehouse depth typically sit toward the upper end of that band, putting fully loaded annual cost (salary plus benefits, payroll taxes, and recruiter fees) well above $250,000 for a single senior hire.
Engineers based in Latin America price materially lower. The all-in cost through Revelo, which includes engineer compensation, compliance framework, benefits administration, PTO, and holidays, runs $86,000–$125,000 per year for senior-level Big Data hires, consistent with Revelo's published Salary Guide anchors for senior backend and data roles, with Big Data specialists with strong Spark or streaming depth typically toward the higher end of that band.
Junior roles start around $56,000–$67,000 all-in; mid-level pricing sits between the junior and senior anchors above. For a current, role-specific figure, use the pricing calculator at revelo.com/pricing. Revelo's pricing is published and transparent, with no large upfront fee and no long-term contract.
| Seniority | LatAm All-In Cost (Revelo) |
|---|---|
| Junior | ~$56,000 – $67,000 |
| Mid-Level | Between junior and senior anchors above |
| Senior | $86,000 – $125,000 |
Note: All-in cost includes engineer compensation, compliance framework, benefits administration, and Revelo's platform fee. Based on Revelo Salary Guide 2024–2026. Use revelo.com/pricing for a role-specific quote.
Why Hire Big Data Developers in Latin America?
Latin America has built real depth in data and backend engineering over the past decade. Brazil's tech sector, anchored in São Paulo and Florianópolis, has produced a generation of engineers who came up on large-scale data problems at banks, fintechs, and e-commerce platforms processing millions of daily transactions. Colombia, Argentina, and Mexico have followed with strong university programs and a growing base of engineers with cloud and streaming experience.
The timezone argument is concrete for Big Data work specifically. Pipeline incidents, schema migrations, and warehouse outages need real-time collaboration. Engineers in Bogotá, Mexico City, Buenos Aires, and São Paulo work within 0 to 2 hours of US Eastern for most of the year, which means your on-call rotation, your data team standups, and your production incidents all happen during business hours.
English fluency is a standard part of Revelo's screening, and engineers who pass it are evaluated specifically on their ability to explain technical decisions to a non-specialist audience, the same skill that matters when a Big Data developer has to walk a product manager through a pipeline outage. Cultural alignment with US engineering practices, including code review norms, sprint cadences, and documentation habits, is high across the region.
How to Evaluate Big Data Candidates
Start by probing systems design judgment. Ask a candidate to walk you through a pipeline they built from scratch: what the ingestion layer looked like, how they handled late-arriving data, and what they'd change if they built it again. A strong answer names specific tradeoffs and owns the decisions; a weak one describes the happy path without acknowledging failure modes.
Second, test distributed systems depth. Ask how they'd tune a Spark job that's spilling to disk on a cluster that should have enough memory. Weak candidates reach for "add more nodes." Strong candidates ask about the data distribution first, suspect skew, and walk through join strategies before touching cluster config.
Third, assess operational maturity. Ask how they monitor a streaming pipeline for data quality drift. A strong candidate has opinions on alerting thresholds, dead-letter queues, and schema evolution. Someone who has only built pipelines and never run them in production will give you a vague answer about dashboards.
Why Big Data Expertise Matters
The demand for Big Data engineering has shifted from "nice to have at scale" to a baseline requirement for any company running a data-informed product or business operation. As event volumes grow and companies layer ML features onto their products, the data infrastructure underneath has to be production-grade, not a collection of cron jobs and manual exports.
What's changed in the past three years specifically is the cost of getting this wrong. A broken pipeline used to mean a delayed report. Now it means corrupted model training data, broken recommendation systems, and product analytics that quietly diverge from reality for weeks before anyone notices. The business exposure has grown in proportion to how much the business depends on the data.
The Big Data specialization narrows this further. A candidate can know Python and SQL without ever having tuned a Spark cluster or debugged a Kafka consumer lag under load, and that gap only shows up once the pipeline is in production and something breaks. Companies that can't staff this role on time find their data roadmaps slipping a quarter at a time while the backlog of analytics requests piles up.
How Revelo Vets Big Data Developers
Every Big Data developer in Revelo's network has cleared a multi-stage screening process that accepts roughly the top 2% of applicants. The screen runs approximately two weeks and covers technical depth, English communication, and professional judgment.
The process starts with recruiter-led pre-screening covering work history, project scope, and stated technical depth. Engineers who pass move to an English fluency assessment, evaluated for both comprehension and the ability to explain technical decisions clearly to a non-specialist audience.
The technical stage is Big Data-specific: candidates are assessed on distributed systems fundamentals, pipeline architecture, and the tools they claim on their profile, including Spark, Kafka, dbt, Airflow, and cloud warehouse platforms. That's followed by a hands-on challenge covering real pipeline or modeling problems, plus a soft-skills evaluation. The process closes with a live senior-engineer interview before any candidate enters the pool.
Deeper technical screening is available on request if your role requires specialist depth beyond the standard screen.
Benefits of Building With Big Data
Why Big Data Wins for Scale and Throughput
Big Data tooling, particularly the Spark and Kafka stack, is purpose-built for workloads that relational databases choke on. When you're processing billions of events a day, running multi-hour batch transforms across terabyte-scale tables, or serving ML features at low latency across distributed systems, the architecture gives you horizontal scale that a single Postgres instance can't provide. The tradeoff is operational complexity; the payoff is throughput that scales with your data volume.
Common Use Cases
Product analytics at scale, real-time fraud detection, recommendation system feature pipelines, log aggregation and anomaly detection, financial reporting across high-transaction systems, and ETL from fragmented SaaS data sources into a centralized warehouse.
Companies Shipping Big Data in Production
Uber built its entire ride pricing and supply-demand model on a Kafka and Flink streaming backbone. LinkedIn open-sourced Kafka precisely because they needed it for their own feed and analytics infrastructure. Netflix runs Spark at petabyte scale for content recommendation. Airbnb built Airflow internally to manage the complexity of its data pipeline dependencies before open-sourcing it.
When Big Data Is the Wrong Choice
If your dataset fits comfortably in memory, or your team is processing a few million rows per day, you don't need a distributed compute layer. Spark has real overhead in cluster management and debugging complexity; DuckDB, Postgres, or a simple dbt project on a managed warehouse will ship faster and cost less to operate. Reach for Big Data tooling when the scale genuinely demands it, not because the architecture looks impressive on a diagram.
Frequently asked questions
Everything you need to know about hiring Big Data developers through Revelo.
How much does it cost to hire Big Data developers through Revelo?
All-in monthly costs run roughly $4,600–$5,600 for junior, $5,800–$7,500 for mid-level, and $7,200–$10,700 for senior developers: engineer compensation, PEO coverage, and Revelo's margin combined. No placement fee, no surprise invoices.
How quickly can I hire Big Data developers through Revelo?
You'll see a curated shortlist of matched, pre-vetted candidates within 72 hours, and most companies make a hire within 14 days of sharing their requirements.
What is Revelo's vetting process for Big Data developers?
Every candidate is identity-checked, skills-tested, and human-screened: technical assessments matched to their stack, soft-skills and English-fluency interviews, and review by in-market recruiting experts before they ever reach your shortlist.
What engagement models does Revelo offer for Big Data developers?
Month-to-month, full-time engagements, with no long-term lock-in. Scale up for a big push or pull back after launch as your roadmap evolves, with a 14-day risk-free trial on every hire.
What happens after I hire Big Data developers through Revelo?
Revelo handles payroll, benefits, taxes, and compliance across 18 countries, and your dedicated account manager stays with you through onboarding and beyond. Your engineer ships code from day one.
How quickly can I hire a Big Data developer through Revelo?
Revelo delivers a shortlist of pre-vetted candidates within 72 hours of receiving your requirements. Most clients complete interviews and extend an offer within two weeks of starting the search. The network is pre-vetted before your search begins, so the 14-day timeline covers the match, interview, and offer process.
What does a Big Data developer cost through Revelo?
Based on Revelo's published Salary Guide, the all-in cost for senior-level Big Data engineers based in Latin America runs approximately $86,000–$125,000 per year, covering engineer compensation, compliance framework, and benefits administration. Junior roles start around $56,000–$67,000 all-in. Use the calculator at revelo.com/pricing for a role-specific quote.
What does the vetting process look like?
Every developer clears a multi-stage screen: profile review, English fluency assessment, a Big Data-specific technical evaluation covering Spark, Kafka, pipeline architecture, and cloud warehouse platforms, a hands-on challenge, and a live senior-engineer interview. Roughly the top 2% of applicants make it through. Deeper screening is available on request for specialist roles.
How does the engagement model work?
Revelo acts as Agent of Record for the independent contractors it engages on behalf of clients. Your Big Data developer works exclusively on your team, in your timezone, under your engineering processes, as an independent contractor rather than a Revelo or client employee. Revelo uses commercially reasonable efforts to structure the engagement, including the contractual framework and payment mechanics, in a manner intended to comply with applicable tax and labor law, administering payroll, compliance, and benefits across 18 Latin American countries as one vendor. There's no long-term contract, and month-to-month terms mean you're not locked in beyond what your team needs.
What is the difference between a Big Data developer and a data engineer?
The two titles overlap heavily in practice, but the distinction comes down to scale and tooling. A data engineer typically owns ETL pipelines, warehouse modeling, and transformation logic, often working with tools like dbt, Airflow, and a managed warehouse such as Snowflake or BigQuery. A Big Data developer operates at a larger scale, usually handling distributed compute with Spark or Flink, streaming architectures with Kafka, and datasets in the terabyte-to-petabyte range where a single-node system can't keep up. Many engineers carry both skill sets, and the line between the roles continues to blur as managed cloud services reduce the infrastructure overhead of distributed compute.
| Dimension | Data Engineer | Big Data Developer |
|---|---|---|
| Typical data scale | Gigabytes to low terabytes | Terabytes to petabytes |
| Primary compute model | Single-node or managed warehouse | Distributed compute (Spark, Flink) |
| Core tooling | dbt, Airflow, Snowflake, BigQuery | Spark, Kafka, Flink, Databricks |
| Streaming focus | Occasional, batch-first | Common, often a core requirement |
| Infrastructure overhead | Lower; largely managed services | Higher; cluster tuning and ops required |
| Overlap | High; many engineers carry both skill sets | |
What if the first hire isn't the right fit?
Revelo includes a 14-day risk-free trial. If the match isn't working within the first two weeks, you move on at no cost and Revelo backfills the role. There's no penalty for a restart, and the search comes back at no additional charge.
For context on cost: the all-in rate through Revelo runs 30–50% less than a comparable US hire, drawn from a pre-vetted network of 400,000+ engineers across Latin America. To see role-specific pricing or start a search to hire big data developers for your team, visit Revelo.
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