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Data that does not fit in a single machine needs Spark. But distributed computing introduces a new class of problems — data skew, shuffle bottlenecks, out-of-memory executors, and job tuning that requires understanding JVM internals as well as Python. Post your Spark job on RemoteAI and hire a developer who writes PySpark that runs efficiently at the terabyte scale, not just the gigabyte scale.
Pre-vetted talent pool
These are the types of Apache Spark developers available on RemoteAI — all pre-vetted, confirmed profiles.
Batch or streaming, approximate data volume (GB or TB per day), the cluster environment (EMR, Databricks, GCP Dataproc), and upstream/downstream systems.
Practitioners who understand partitioning, shuffle optimization, and cluster tuning — not just developers who have written PySpark DataFrame code on small datasets.
Look for candidates who can show a before/after on a Spark job optimization or explain a complex execution plan. That is the real signal.
Agree a rate and pay your Spark developer directly. RemoteAI takes nothing.
Hiring a apache spark developer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
Apache Spark developers on RemoteAI work across batch processing, structured streaming, and ML pipeline design at scale. Core skills: writing performant PySpark or Scala Spark jobs, optimizing DataFrames using broadcast joins, partition tuning, and caching strategies, building streaming pipelines with Spark Structured Streaming or Spark + Kafka, integrating with cloud storage (S3, ADLS, GCS) and data warehouses, and deploying jobs on EMR, Databricks, or YARN clusters. Strong Spark developers read execution plans — they identify shuffle operations, tune executor memory and parallelism, and fix performance problems before a job fails at scale.
A well-structured job post attracts the right apache spark developers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
Batch or streaming, approximate data volume (GB or TB per day), the cluster environment (EMR, Databricks, GCP Dataproc), and upstream/downstream systems.
Practitioners who understand partitioning, shuffle optimization, and cluster tuning — not just developers who have written PySpark DataFrame code on small datasets.
Look for candidates who can show a before/after on a Spark job optimization or explain a complex execution plan. That is the real signal.
Agree a rate and pay your Spark developer directly. RemoteAI takes nothing.
Looking to browse talent directly?
View vetted Apache Spark Developer Jobs and contact them directly — no job post needed.
Batch vs streaming, data volume, cluster environment (EMR/Databricks/Dataproc), language preference (PySpark vs Scala), upstream/downstream systems, and latency or throughput requirements.
PySpark is more common for data engineering and ML use cases and integrates naturally with Python data tools. Scala Spark has lower serialization overhead and is better for pure performance-critical pipelines. Most RemoteAI Spark developers are strong in PySpark; mention Scala explicitly if it is a hard requirement.
Free to post, zero commission on contracts. You pay your developer directly.
Apache Spark developers on RemoteAI typically charge $75–$160/hour. Scala Spark and streaming specialists are at the higher end.
Sign up free and describe the processing scale, cluster environment, and whether batch or streaming. RemoteAI surfaces Spark developers with the relevant ecosystem and scale experience. Most posts have a shortlist within 24 hours.