A shortlist typically within 72 hours, usually contributing inside two weeks, and 30-day replacement support if the fit is wrong (terms in your agreement).
Shortlist typically in 72 hours · 30-day replacement support · Month-to-month
Spark jobs are usually written when data is small. They run in minutes, pass their tests and go to production. Then volumes grow, a join starts shuffling hundreds of gigabytes, one skewed key keeps a single task running for hours, and the nightly batch finishes after the business day has started.
A senior Spark engineer understands what happens under the hood: how data is partitioned, when a shuffle happens and why one executor runs out of memory. That knowledge is the difference between adding more hardware and fixing the job.
Partitions, shuffles, broadcast joins and memory management.
Production code in either language, with tests.
Structured Streaming, Kafka, state and checkpointing.
Delta Lake, Iceberg or Hudi used properly.
Databricks, EMR, Dataproc or on-premise clusters.
Right-sized clusters and jobs that use them efficiently.
Missed deadlines. Batches that overrun delay reports, feeds and downstream systems.
Expensive clusters. Throwing more nodes at a skewed or poorly partitioned job costs money and rarely fixes it.
Fragile streaming. Streaming jobs without proper checkpointing and state management lose or duplicate data after failures.
Every engineer we put forward has at least five years of hands-on experience. Within that, here is how the levels differ.
Senior Engineer (5–8 years). Writes batch and streaming jobs in Scala or PySpark, tunes partitions, joins and memory, and works with Kafka, Delta Lake or Iceberg. Comfortable on Databricks, EMR or Dataproc.
Lead / Architect (8+ years). Designs data platforms, streaming architecture, table formats and job orchestration, and leads migrations from Hadoop or legacy ETL.
Performance tuning. Fixing slow and failing jobs by addressing skew, shuffles and memory settings
Streaming pipelines. Structured Streaming with Kafka or Kinesis, exactly-once sinks and monitoring
Hadoop migrations. Moving Hive and Spark workloads to cloud platforms and modern table formats
Scala code bases. Maintaining and extending Scala Spark applications with tests
Week one. They profile your slowest jobs using the Spark UI and review cluster and table setup.
Weeks two and three. First fixes to the worst jobs, with before and after runtimes and costs recorded.
Week four. A written plan for further tuning, streaming reliability or migration, ranked by impact.
Adding nodes instead of fixing jobs. More hardware hides problems for a while and raises the bill.
Hiring for PySpark when the code is Scala. Scala Spark applications need engineers comfortable with Scala and the JVM.
No testing for data jobs. Unit tests and sample-data checks catch logic errors before they reach production.
Rates depend on seniority, scope and engagement length. See the cost guide for market ranges, or tell us the role and we will give you a firm number.
Tell us the role and we will typically send a shortlist within 72 hours.
Tell Us the RoleTell us the role. Three to five profiles, typically within 72 hours.