Data Engineering
Hire senior Apache Spark engineers
Spark is still the engine under the serious data platforms. Our seniors read query plans before they read dashboards: they tune partitions, kill shuffles, and turn eight-hour jobs into forty-minute ones.
Shortlist of around 3 senior profiles in under 5 business days
- Seniority floor
- 7+ years owning production systems
- Time zone
- Full overlap with US Eastern to Pacific
- Shortlist
- Around 3 curated profiles in under 5 business days
- Engagement model
- Embedded in your team, no resellers or middlemen
- Pricing
- All-inclusive rate, around 35% below the US onshore equivalent
How we vet
What senior Apache Spark actually looks like
Execution internals: stages, shuffles, broadcast joins, and memory management
PySpark and Spark SQL fluency, plus Structured Streaming for near-real-time
Skew diagnosis and salting strategies on real datasets
Deployment reality: EMR, Databricks, Dataproc, and Kubernetes operators
The roles that master Apache Spark
Apache Spark questions, answered
When is Spark the wrong tool?
For small data. A senior will tell you when pandas, Polars or plain SQL beats spinning up a cluster, saving real money.
What is the most common Spark performance issue you fix?
Data skew and unneeded shuffles. Seniors spot them in the Spark UI and fix them at the partitioning level, not by throwing bigger instances at the job.
Do your engineers write PySpark or Scala Spark?
Both. PySpark is the most requested by US teams; Scala appears in older enterprise codebases.
Usually paired with Apache Spark
Keep reading about hiring for Apache Spark
Need senior Apache Spark on your team?
Tell us the context. A curated shortlist of senior profiles in under five business days, working in your time zone.