LATAM - Future Opportunities – Senior Data Engineers

Personal Information

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Email ^* Phone ^*

Country ^* Postal Code ^*

Professional Details

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Personal Information

Do you currently reside in Latin America (Argentina, Brazil, Chile, Costa Rica, El Salvador, Paraguay, Uruguay, or others) What City and Country? ^*

Please detail your familiarity and experience with the technology industry, and specifically within the data space (industry, key players, software tools, etc.) if you have any. ^*

When are you available to start a new role? If your require notice period, please specify the number of weeks required. ^*

Why Infostrux? ^*

Are you legally authorized to work in your country of residence with no restrictions? ^*

What are your annual salary expectations for this role? ^*

How did you hear about us? If referred by an Infostrux employee, please include name. ^*

What type of engineering roles or projects are you most interested in for future opportunities at Infostrux? (Provide details on your areas of interest, whether it’s working on specific technologies, tackling particular types of challenges, or being part of certain teams). ^*

What are your key technical skills and proficiencies? (List specific programming languages, tools, frameworks, cloud platforms, or other technologies you have experience with). ^*

Are you fluent in French? ^*

Describe a specific instance where a client's poor architectural choices in Snowflake or Databricks led to a critical failure or a massive cost overrun. What was the 'smoking gun,' and what internal resistance did you face when trying to fix it? ^*

During a migration to Snowflake, what was one feature or performance 'win' you promised the client that actually proved much harder to deliver once you were in the weeds? How did you pivot your technical strategy to meet their expectations? ^*

Identify a specific technical hurdle in the last 5 years—perhaps a library, an API, or a distributed computing concept—where your first three attempts to solve it failed. What was your specific 'aha!' moment, and what does your 'mental model' for that technology look like now? ^*

Looking at the 2026 landscape of data engineering, identify one highly-touted trend you believe is actually a distraction for most mid-sized enterprises. How are you advising your clients to steer clear of it while still future-proofing their stack? ^*

With the explosion of new tools, walk us through your 'vetting' process for a new piece of tech. Tell us about a tool you spent time researching in the last year but ultimately rejected. What specific data point or test result led to that 'no'? ^*