Get Scala Developers
Functional and object-oriented JVM engineers from Brazil, Argentina, and Colombia who build data pipelines, distributed systems, and high-throughput backends in Scala. They work in Akka, Spark, Play Framework, and ZIO — not just on tutorials.
Scala engineers in the US command $165k-$210k. LATAM equivalents deliver at $70k-$105k — a meaningful difference for data-intensive projects where Scala is used specifically for throughput.
LATAM's UTC-3 to UTC-5 overlaps with US teams for 4-8 hours. Scala data pipeline work often needs coordination with data scientists and ML engineers — real-time availability matters.
Scala developers have to explain type system choices, effect systems, and concurrency models to mixed teams. LATAM engineers in this space are articulate about design tradeoffs.
Brazil and Argentina have strong JVM communities. Scala developers here often have Java and Haskell backgrounds that give them a solid foundation in type theory and functional programming.
Scala is the native language for Apache Spark. LATAM data engineers who specialize in large-scale data processing often have years of production Spark experience.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 90-minute live session probing Scala type system knowledge — implicits, typeclasses, higher-kinded types — and how they apply in practice, not just in theory. Candidates implement a ZIO or cats-effect based service with proper error handling and resource management. We cover Spark: DataFrame vs Dataset APIs, handling skewed joins, and Spark SQL optimization. For data pipeline candidates, we discuss watermarking in Structured Streaming and when to use Kafka + Spark vs Flink. The session includes a code review of a given snippet with implicit resolution issues and performance anti-patterns.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
4+ years experience
Buenos Aires, Argentina
7+ years experience
Curitiba, Brazil
10+ years experience
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Request matched profilesWhat to expect
Scala developers in our network bring genuine type system and functional programming discipline to their work. They don't use implicits carelessly, they test effects properly, and they optimize Spark jobs based on the actual execution plan rather than guesses. They use AI tools to handle the boilerplate side of Scala development.
GitHub Copilot and Cursor for sbt build configuration, ScalaTest spec scaffolding, and schema definition boilerplate. Less time on setup, more on architecture.
AI-assisted documentation from Scaladoc comments — useful for large codebases where documentation has fallen behind the implementation.
They read the Spark query plan before claiming an optimization is needed. They don't tune what they haven't measured.
Effect system decisions are documented: why ZIO vs cats-effect vs Monix, and what it means for the team that follows. The choice doesn't live only in their head.
They communicate Spark cluster sizing decisions and cost implications before a job goes to production. A misconfigured EMR cluster is an expensive mistake.
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