Looking to hire a PySpark or Apache Spark developer? RemoteAI connects you directly with Spark engineers, available dedicated full-time or as remote contractors, for batch ETL pipelines, Spark Structured Streaming, Databricks integration, and large-scale data engineering.
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Looking to hire a PySpark or Apache Spark developer? RemoteAI connects you directly with Spark engineers, available dedicated full-time or as remote contractors, for batch ETL pipelines, Spark Structured Streaming, Databricks integration, and large-scale data engineering.
Apache Spark engineers design and operate distributed data pipelines that process datasets too large for single-machine tools like pandas or SQL. They write PySpark or Scala Spark jobs for batch ETL, Spark Structured Streaming for near-real-time processing, and Spark MLlib for distributed ML. They manage cluster configuration (Databricks, EMR, Dataproc), optimize job performance through partitioning, caching, and broadcast join strategies, and integrate with Delta Lake, Hive, and cloud data lakes. Many also handle Delta Live Tables pipelines on Databricks.
Finding the right Apache Spark engineer requires more than evaluating technical skills — it means identifying someone who ships production-ready work and understands your business goals. Here is exactly how to do it.
Spark is the right tool when your data exceeds what pandas can handle in memory (typically 10 GB+). Define your data volume, processing frequency (batch vs. streaming), and downstream consumers.
Ask about their approach to handling data skew, choosing between wide and narrow transformations, and when to use broadcast joins. These reveal whether they have debugged real production Spark performance issues.
Spark is only as useful as the cluster managing it. Ask about their experience with Databricks, EMR, or Dataproc, including autoscaling configuration, spot instance usage, and cluster cost monitoring.
A review of your existing Spark jobs (identifying slow stages, data skew, and inefficient joins) is a practical first deliverable with immediate cost and performance impact.
Use Spark when your data exceeds in-memory capacity (typically 10 GB+), when you need distributed processing across a cluster, or when you need Structured Streaming. For smaller datasets, pandas, dbt, or a data warehouse SQL engine is faster and cheaper.
Senior engineers with production cluster management, streaming and Delta Lake experience charge more than mid-level Spark engineers. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
PySpark is the Python API for Spark: more widely adopted, better for data science workflows. Scala Spark is the native API: marginally faster and better for complex custom operations. For most data engineering and ML use cases, PySpark is sufficient.
Yes. Spark Structured Streaming processes data in micro-batches with latencies from seconds to minutes. For sub-second latency requirements, Apache Flink or Kafka Streams is a better fit.
No. Spark runs on any cluster: AWS EMR, Google Dataproc, Azure HDInsight, or a self-managed Kubernetes cluster. Databricks provides managed infrastructure, Delta Lake, MLflow, and a polished UI: a significant reduction in operational overhead for teams without dedicated platform engineers.