Hire a Top Data Engineer in LatAm. Same Quality. 50% Less.

• Develop, construct, test, and maintain architectures, such as databases and large-scale processing systems.
• Ensure architecture will support the requirements of the business and data scientists.
• Assemble large, complex sets of data that meet non-functional and functional business requirements.
• Identify ways to improve data reliability, efficiency, and quality.
• Use data to discover tasks that can be automated.
• Build algorithms and prototypes.
• Conduct complex data analysis and report on results.
• Prepare data for predictive and prescriptive modeling.
• Collaborate with data architects, modelers, and IT team members on project goals.
• Use a variety of languages and tools (e.g., scripting languages) to marry systems together.

• Proven experience as a Data Engineer, Software Developer, or similar role.
• In-depth understanding of database structure principles.
• Experience gathering and analyzing system requirements.
• Knowledge of data mining and segmentation techniques.
• Expertise in SQL and other programming languages (e.g., Python, Java, Scala).
• Familiarity with various ETL techniques and frameworks, such as Flume.
• Experience with Big Data tools (e.g., Hadoop, Spark, Kafka).
• BSc/BA in Computer Science, Engineering, or relevant field; graduate degree in Data Engineering or other related field is a plus.
• Analytical skills to work on complex, multifaceted projects.
• Effective communication and collaboration skills.

What Kind of Data Engineer Does Your Company Need?

A Data Engineer building a cloud warehouse for a growing SaaS company faces different challenges from someone managing real-time event streams or modernizing outdated data infrastructure.

The right candidate depends on your existing data architecture, the systems your company relies on, and the technical responsibilities they'll own.

Some experienced Data Engineers can handle several of these responsibilities, particularly when they've developed data systems through multiple stages of business growth.

However, experience operating similar production workloads matters more than familiarity with every tool in the modern data stack.

For example, a company using Snowflake, dbt, and Airflow may benefit from someone experienced in warehouse transformations and orchestration.

A business processing large volumes of real-time events may need deeper expertise in Kafka, Spark, and distributed processing.

Before hiring, identify which pipelines, databases, warehouses, and cloud environments your developer will own.

You'll also want to establish whether the position involves building new infrastructure, improving existing systems, or both.

How Should a Remote Data Engineer Fit Into Your Team?

A remote Data Engineer can take ownership of pipelines, data infrastructure, integrations, and production reliability while collaborating with your analytics, software engineering, and data science teams.

The arrangement works best when the engineer understands how data moves through your company and who depends on the information they deliver.

Define Ownership of Data Pipelines and Infrastructure

Your Data Engineer may develop ingestion pipelines, manage data transformations, configure orchestration workflows, and troubleshoot problems affecting downstream applications.

However, your company should establish who approves architectural changes and which systems the engineer can modify independently.

For example, a SaaS company might have its Data Engineer manage pipelines that move billing, product usage, and CRM information into Snowflake.

The engineer owns ingestion reliability and approved transformations, while the analytics team defines how those datasets support business metrics.

An engineering lead may review major changes affecting infrastructure, security, or shared services.

Establish who owns data ingestion, transformation logic, infrastructure configuration, and production releases.

This helps prevent confusion when a change to an upstream system affects reports or applications managed by another team.

Establish Data Quality and Monitoring Responsibilities

A data pipeline can complete its scheduled run while still producing incomplete or incorrect information.

For example, an upstream application may change a field name, duplicate records, or stop sending certain events.

Your Data Engineer should understand how to identify these problems and maintain confidence in the datasets your company uses.

Before assigning responsibilities, establish expectations for:

• Data freshness: How frequently important datasets should be updated.
• Data completeness: How missing records, unexpected volumes, and incomplete loads are detected.
• Data accuracy: How important calculations and transformations are validated.
• Schema changes: How upstream changes are identified and handled.
• Pipeline monitoring: Which failures, delays, and anomalies generate alerts.
• Recovery procedures: How failed jobs are retried and historical data is reprocessed.
• Documentation: How dependencies, definitions, and operational procedures are recorded.

For example, if a daily revenue pipeline suddenly loads fewer transactions than expected, the engineer should be able to determine whether the issue originated in the source application, ingestion process, or downstream transformation.

Define which datasets are business-critical and agree on appropriate freshness, quality, and recovery expectations.

Those requirements help your engineer prioritize their work and design monitoring that reflects business needs.

Coordinate With Analytics, Product, and Software Engineering

Data Engineers frequently support teams with different technical and business priorities.

A Data Analyst may need reliable warehouse tables for executive reporting.

A Data Scientist may require historical datasets for model development.

A product engineering team may need event data to support application functionality.

Your Data Engineer should understand these requirements and help establish dependable interfaces between systems.

For example, when a product team introduces a new customer activity event, the engineer may need to coordinate the event structure, ingestion process, and downstream data availability.

If the event changes later, affected teams should know what to expect.

Give your Data Engineer access to the teams responsible for producing and consuming the data.

Regular collaboration helps them recognize important dependencies and design infrastructure that supports actual business workflows.

Establish Security and Cloud Cost Responsibilities

Data infrastructure may store customer records, employee information, financial transactions, and other sensitive business data.

Your Data Engineer should follow your company's security policies when accessing, processing, and storing information.

Relevant responsibilities may include managing service permissions, applying approved access controls, protecting credentials, and supporting data retention requirements.

Your organization should also define who approves infrastructure changes and manages cloud spending.

For example, poorly configured data processing jobs can increase compute costs without delivering additional business value.

An experienced engineer should understand how to investigate inefficient queries, unnecessary processing, excessive storage, and avoidable data duplication.

Establish clear permissions, deployment procedures, and cost monitoring responsibilities before assigning ownership of production systems.

For complex security or infrastructure decisions, your Data Engineer may collaborate with platform engineering, IT, and security specialists.

Related Roles to Consider

Depending on your company's data requirements, another professional may be a better fit or a useful addition to your team.

• Analytics Engineer: Best for companies that need ownership of analytics-ready data models, dbt transformations, metric definitions, testing, and documentation within an existing data platform.
• Data Architect: A strong fit when your organization needs broader data architecture design, integration standards, platform decisions, and long-term technical direction.
• Data Analyst: Focuses on investigating business performance, analyzing trends, answering questions, and communicating findings using available data.
• BI Analyst: Supports recurring business reporting, dashboards, KPI visibility, and tools such as Power BI, Tableau, or Looker.
• Machine Learning Engineer: Appropriate when your company needs dedicated expertise in machine learning development, model pipelines, deployment, and monitoring.
• Database Administrator: Focuses on database availability, maintenance, backups, access management, query performance, and operational reliability.

A Data Engineer is generally the strongest choice when your company needs dedicated ownership of data movement, infrastructure, pipelines, and processing reliability.

If your pipelines already work and your main challenge is creating reliable business-facing data models, an Analytics Engineer may be more appropriate.

If leadership needs better analysis and reporting from available datasets, a Data Analyst or BI Analyst may provide more immediate value.

South can help you identify professionals across these roles based on your data stack, infrastructure requirements, and business priorities.

How to Evaluate Data Engineer Candidates

A candidate may list Snowflake, Databricks, Airflow, Spark, SQL, and Python on their résumé.

Those technologies provide useful context, but the most valuable evidence is how they design reliable pipelines, investigate data problems, and maintain systems that other teams depend on.

A realistic technical assessment can help reveal those abilities.

Consider giving shortlisted candidates the following scenario:

Your company collects customer, subscription, and payment data from several systems and loads it into a cloud data warehouse every night. Finance recently noticed that monthly revenue totals don't match its billing platform. Some records appear twice, a portion of yesterday's payments is missing, and one scheduled pipeline has been failing intermittently.

How would you investigate the discrepancies, recover the missing data, and improve the pipeline?

Provide a simplified system diagram, sample source tables, basic pipeline logs, and fictional business requirements.

Ask candidates to explain what they'd investigate first, how they'd validate the corrected data, and which improvements they'd make to prevent similar issues.

Use the following evaluation scorecard:

Pay particular attention to how candidates separate a successful pipeline run from a correct business result.

A pipeline may finish without errors even when duplicate records inflate revenue or missing transactions produce incomplete reporting.

A strong Data Engineer should describe how they'd reconcile the dataset with the source system and verify the corrected results.

They should also understand how to rerun a failed or incomplete pipeline without creating additional duplicates.

For senior candidates, ask them to describe a production data system they've personally designed or maintained.

Have them explain the architecture, data volumes, performance requirements, major incidents, and how they improved reliability.

You should also ask how they approached infrastructure costs and whether their decisions affected other teams.

For roles involving streaming systems, replace part of the exercise with a scenario covering late-arriving events, processing delays, or duplicate event delivery.

Match the assessment to your actual architecture rather than requiring every candidate to demonstrate expertise in unrelated technologies.

What Should a Data Engineer Accomplish in the First 90 Days?

The first three months should demonstrate whether your new Data Engineer understands your data environment, can manage important pipelines, and contributes to more reliable infrastructure.

First 30 Days: Understand the Data Stack and Existing Problems

Your new hire should become familiar with your data sources, pipelines, warehouse, cloud infrastructure, orchestration tools, and data consumers.

They should also review the existing monitoring process, recurring incidents, deployment procedures, and major technical priorities.

A useful first milestone is completing an assessment of an assigned data workflow and resolving a defined technical issue.

That might involve fixing a failed ingestion job, improving a SQL transformation, or adding a missing data quality check.

The priority is understanding how information moves through your systems and demonstrating reliable technical execution.

Days 31–60: Take Ownership of Pipelines and Improvements

By the second month, the Data Engineer should begin managing assigned pipelines or data domains more independently.

That may involve improving orchestration, implementing a new data source, addressing recurring reliability problems, or preparing a historical backfill.

They should also contribute to documentation, code reviews, and testing standards.

For business-critical datasets, the engineer should work with relevant stakeholders to agree on data freshness and quality expectations.

The objective is consistent ownership supported by reliable development and operational practices.

Days 61–90: Demonstrate Measurable Data Engineering Value

By the third month, your Data Engineer should be contributing regularly to infrastructure improvements and maintaining the systems they're responsible for.

Useful performance indicators include:

• Successful completion rate of assigned pipeline runs
• Freshness of business-critical datasets against agreed targets
• Number and severity of recurring data quality incidents
• Time required to identify and resolve failed data workflows
• Accuracy and completeness of recovered or backfilled data
• Progress on approved integrations or migration milestones
• Improvement in targeted processing times or query performance
• Cloud resource consumption for comparable data workloads
• Quality of tests, documentation, and technical handovers

Evaluate these indicators using a baseline established early in the engagement.

For example, a Data Engineer maintaining an established warehouse may be assessed on pipeline reliability, processing costs, and data quality.

Someone working on a major migration may be evaluated against successful validation, pipeline cutovers, reconciliation, and documented recovery procedures.

A successful first 90 days should establish dependable data infrastructure ownership, clearer visibility into data issues, and measurable progress on the technical priorities that motivated the hire.

How South Helped a Hardware Technology Company Hire a Data Engineer in 17 Days

A U.S.-based B2B manufacturer and distributor specializing in hardware technology needed to strengthen its data infrastructure as the business grew.

The company wanted a Data Engineer who could design, build, and maintain a data warehouse using SQL Server and Microsoft Azure.

Its priorities included improving data processing, ensuring information was available for analytics, and supporting infrastructure that could accommodate future growth.

The company had attempted to recruit locally but faced compensation and hiring challenges.

The Hire

South helped the company hire Valentino, a Data Engineer from Argentina, in just 17 days.

Valentino brought three years of remote Data Engineering experience, including work on cloud migrations, data warehouse architecture, and ETL pipelines.

His background included experience with PwC and international technology companies, alongside proficiency in SQL, Python, Azure Data Factory, Azure DevOps, and Databricks.

His previous work included:

• Migrating legacy data infrastructure to Azure Databricks
• Developing ETL pipelines to improve data processing
• Implementing data warehouse architecture using Delta Lake
• Supporting scalable data systems and engineering workflows

The results included:

• Time to hire: 17 days
• Role: Full-time Data Engineer
• Location: Argentina
• Monthly salary: $3,400
• Reported savings: 68% against the case study's estimated U.S. salary
• Technical environment: SQL Server, Azure, ETL pipelines, and cloud data infrastructure

According to South's published case study, Valentino began contributing to data system improvements, ETL optimization, and the company's analytical infrastructure.

Why this matters: The company needed someone who understood its cloud environment and could contribute to reliable data engineering work from an established technical foundation.

South identified a candidate with relevant experience across data warehouses, cloud migrations, and pipeline development.

This is a direct Data Engineer placement, making it particularly relevant to companies evaluating South for similar technical positions.

Hire a Data Engineer in Latin America With South

Your company's dashboards, reporting systems, and analytical applications depend on the infrastructure that delivers their data.

As your business grows, a dedicated Data Engineer can help your team maintain reliable pipelines, introduce new data sources, and improve the systems supporting important decisions.

South helps U.S. companies hire full-time, pre-vetted Data Engineers from Latin America, matched to their cloud platforms, data architecture, technical requirements, and level of experience.

Whether you need help with Snowflake, Databricks, Azure, BigQuery, SQL pipelines, orchestration, or data infrastructure modernization, we can help you find professionals with relevant hands-on experience.

You'll benefit from overlapping U.S. working hours, English-screened candidates, one consolidated monthly invoice, no minimum commitments, and a free replacement if the hire doesn't work out.

Schedule a call with South to find a Data Engineer who fits your infrastructure needs and hiring budget.

Frequently Asked Questions (FAQs)

Should I Hire a Data Engineer or an Analytics Engineer?

A Data Engineer is generally the stronger choice when your company needs someone to build and maintain data pipelines, ingestion systems, warehouses, or data infrastructure.

An Analytics Engineer may be more appropriate when your infrastructure already delivers reliable source data and the main requirement involves transformations, business-facing data models, tests, and metric definitions.

The responsibilities can overlap, especially on smaller data teams.

Choose based on whether your main challenge involves moving and processing data or preparing it for consistent analytical use.

Does a Data Engineer Need Experience With Our Exact Cloud Platform?

Experience with your existing platform can reduce the time required for a new hire to begin contributing.

For example, a company using Azure Data Factory, Azure SQL, and Databricks may benefit from candidates who've worked directly with those services.

However, experienced engineers can often transfer their understanding of pipeline architecture, SQL, data modeling, and reliability between cloud environments.

Prioritize platform-specific experience when the position involves immediate ownership of complex production systems.

Should I Hire a Junior, Mid-Level, or Senior Data Engineer?

Junior Data Engineers are generally suited to defined transformations, pipeline maintenance, data validation, and smaller development tasks under experienced supervision.

Mid-level engineers can independently manage assigned pipelines, integrate data sources, maintain orchestration workflows, and troubleshoot recurring issues.

Senior Data Engineers may be more appropriate for platform architecture, large migrations, complex performance problems, streaming systems, and technical leadership.

Choose seniority based on the complexity of your environment and the decisions the engineer will make independently.

Does a Data Engineer Need Python and SQL Experience?

SQL is particularly important for warehouse-based data engineering, transformations, validation, and performance analysis.

Python is widely used for data ingestion, automation, custom integrations, and processing workflows.

Some environments rely more heavily on Java, Scala, or other programming languages, especially for specialized distributed processing.

Define the required language proficiency according to your production systems rather than creating a universal list.

Should Our Data Engineer Know Snowflake, Databricks, and BigQuery?

The most important platform is the one your company uses.

A Data Engineer maintaining a Snowflake environment should understand its data processing and performance characteristics.

A Databricks-focused position may require more experience with Spark, Delta Lake, and distributed processing.

BigQuery experience may be particularly valuable for companies using Google Cloud.

Strong production experience in your primary platform is more useful than superficial familiarity with several alternatives.

Can One Data Engineer Manage Both Pipelines and Analytics?

An experienced Data Engineer may be able to build pipelines, prepare warehouse datasets, and support some analytical reporting.

However, ongoing analytical work requires time for business questions, metric interpretation, and stakeholder communication.

If your company has substantial needs across both infrastructure and reporting, separate Data Engineering and analytics responsibilities can provide clearer accountability.

The appropriate team structure depends on workload and technical complexity.

When Does a Data Engineer Need Streaming Experience?

Streaming expertise is valuable when your company processes events or transactions that need to become available with low latency.

Examples include operational monitoring, event-driven applications, fraud detection, and certain real-time analytical systems.

For companies relying primarily on scheduled reporting and daily data updates, batch processing experience may be sufficient.

Establish actual latency requirements before making streaming technologies mandatory.

What Security Experience Should a Data Engineer Have?

Relevant experience may include cloud identity and access management, service accounts, encryption, credential handling, data permissions, and secure processing of sensitive information.

The developer should understand how to work within approved environments and apply appropriate access restrictions.

For regulated or sensitive data, your company's security and compliance teams should establish the applicable requirements and review procedures.

How Should I Measure a Data Engineer's Performance?

Useful indicators include pipeline reliability, dataset freshness, data quality, incident resolution, processing performance, and infrastructure costs.

Measures should reflect the systems the engineer owns.

For example, a professional working on a migration should be evaluated against successful reconciliation and cutover milestones, while a reliability-focused engineer may be assessed on reducing recurring failures and improving recovery procedures.

Use agreed baselines and consider dependencies outside the engineer's direct control.

Can a Remote Data Engineer Work With Our U.S. Technical Team?

Yes. Remote Data Engineers can collaborate with U.S.-based analysts, engineering teams, data scientists, and cloud specialists.

Overlapping working hours can support architecture reviews, pipeline troubleshooting, deployment coordination, and incident response.

Your company should establish access permissions, communication processes, technical documentation, and clear ownership of production systems.

What Information Should I Prepare Before Hiring Through South?

Start with your cloud provider, data warehouse, main data sources, orchestration tools, and the pipelines or platforms your new hire will manage.

You should also define approximate data volumes, freshness requirements, recurring technical problems, security needs, expected seniority, and the level of production ownership involved.

South can use those requirements to identify candidates whose backgrounds match your data engineering priorities.