Hire Dedicated Apache Spark Engineers — India, US, UK & Australia
Hire pre-vetted Apache Spark engineers who build large-scale batch and streaming data pipelines that process terabytes reliably. Golonex places dedicated India-based Spark engineers with US, UK, Australian, and Indian teams — working in your timezone, under NDA, integrated within one week and with no lock-in.
What a Apache Spark Engineer does
An Apache Spark engineer designs and tunes distributed data processing jobs: ETL at scale, aggregations, joins across huge datasets, and streaming pipelines. Our engineers write performant PySpark and Scala Spark, work in Databricks and on cloud clusters, and handle partitioning, caching, and query optimisation to keep jobs fast and cost-efficient.
Core expertise
What a Golonex Apache Spark Engineer builds for you
How it works
Engagement models
Based in India, working in your timezone. Onsite options available across the US, UK, and India.
Category
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Data, AI & ML Engineers
The Golonex difference
Named, pre-vetted engineers
Every Apache Spark Engineer is screened for technical depth, communication, and reliability before you meet them — a sub-30% pass rate at the technical stage.
Integrated within 1 week
From enquiry to working team member in 5 business days. We handle onboarding, NDA, and access logistics.
Your timezone
India-based talent working overlapping hours with US, UK, and Australian teams. Daily standups and real-time collaboration, agreed upfront.
NDA-protected, no lock-in
Full IP and confidentiality protection as standard. Month-to-month engagements — scale up or down as your project evolves.
Hiring a Apache Spark Engineer — FAQs
How quickly can I hire an Apache Spark engineer through Golonex? +
Typically within one week. We shortlist pre-vetted Spark engineers matched to your stack — PySpark, Scala, or Databricks — you interview them, and the selected engineer is onboarded within 5 business days.
Do your Spark engineers work in my timezone? +
Yes. Our engineers are India-based but work overlapping hours with US, UK, and Australian teams. The overlap is agreed before the engagement so pipeline reviews and pairing happen in real time.
Can they optimise slow or expensive Spark jobs? +
Yes. A frequent engagement is performance tuning — diagnosing skew, excessive shuffles, and poor partitioning, then rewriting jobs and cluster configs to cut runtime and cloud cost. Our engineers profile before they change anything so improvements are measurable.
Do they work with Databricks and cloud platforms? +
Yes. Our Spark engineers work across Databricks and native cloud clusters on AWS EMR, Azure, and GCP, including Delta Lake and lakehouse patterns. They fit into your existing orchestration, whether that is Airflow, Databricks Workflows, or another scheduler.
How do you vet Apache Spark engineers? +
Every candidate passes a technical assessment on real distributed-processing scenarios plus an architecture review, a communication assessment, and reference checks from at least two prior engagements. Our technical pass rate is under 30 percent.
Ready to hire a Apache Spark Engineer?
Tell us the role, stack, and timeline. We'll match you with a named, pre-vetted engineer — integrated within 1 week.