Scala specialists for your dedicated team.

Where Scala fits in a long-term product team.

Scala's place in data work is largely Spark's doing. The engine is written in it, the Scala API is the most complete and the fastest, and understanding Spark's behaviour at depth usually means reading Scala. For high-throughput jobs the difference against the Python API is measurable.

The language allows a very wide range of styles, from lightly functional Java to deeply abstract type-level programming, and a codebase that has drifted toward the latter is hard for others to maintain. Agreeing and holding a restrained style is more consequential on Scala than on most languages.

What your dedicated team does with Scala.

Scala expertise is comparatively rare, which affects how a team is composed. A codebase that only one person can maintain is a risk regardless of how good that person is.

Building depth across a team, so knowledge never rests with a single person, is what the Talent Success and Academy programme is for.

Common Scala use cases.

• Spark jobs where throughput matters The native API, where the Python overhead is measurable at volume.
• Type safety across pipeline stages Schema mismatches caught at compile time.
• A style restrained enough to maintain Conventions agreed, so the codebase does not become one person's dialect.

Adding Scala skills to your team.

Scala work sits with a dedicated data engineering team, with a codebase style agreed by the team.

Every Scala specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.