Skill specialists
Hire Apache Spark Engineers
Find data engineers who build large-scale distributed data processing pipelines with Apache Spark — PySpark, Structured Streaming, and ML feature engineering at petabyte scale.
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Raymond Blaha
Yassin Salimu
Abhishek
Angelo Leto
Hiring guide
Why companies hire Spark experts
Apache Spark is the standard for distributed large-scale data processing in AI companies. Spark engineers build the data foundation that feeds ML training pipelines, powers real-time feature stores, and processes petabyte-scale datasets.
Popular projects
- Process petabyte-scale datasets for ML model training
- Build real-time streaming feature pipelines for online inference
- Migrate slow ETL jobs to distributed PySpark for 10x speed gains
- Design a scalable feature store feeding production ML models
What to look for
- Production PySpark experience with performance tuning
- Understanding of Spark execution model (partitioning, shuffling)
- Experience with Spark on Databricks, EMR, or Dataproc
Frequently asked questions
When do AI companies need Apache Spark?
When your data volumes exceed what single-node tools like Pandas can handle reliably. Spark is typically chosen when datasets exceed tens of GB or when real-time streaming processing is required.
Do Spark engineers need to know Scala or is Python enough?
Python (PySpark) is sufficient for most production workloads. Scala offers better performance for custom low-level Spark operations. Most engineers on RemoteAI use PySpark — check individual profiles if Scala Spark is a requirement.
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