Do we actually need Spark, or is our pandas job just badly written?

A fair question and often the right one. We will look at the existing job first — a large share of "we need Spark" cases are solved by fixing dtypes, chunking, or pushing work into SQL. We would rather tell you that than sell you a cluster.

Can you work with our existing Databricks or EMR setup?

Yes. Our engineers work across Databricks, AWS EMR, and GCP Dataproc, and can work within whatever orchestration and access model you already have rather than requiring a new platform.

How do you make sure a migrated job produces the same results?

We run old and new in parallel against the same input and reconcile the outputs row by row before cutover. Migrations that skip this step are where silent data drift comes from.

Hire PySpark Developers

Distributed data engineers for the point where a single machine stops being enough.

Get Matched with a Developer

PySpark is what you reach for when the dataset no longer fits in memory and the job no longer finishes overnight. It is also easy to use badly — a Spark job written like pandas code will run, slowly and expensively, and the cloud bill is usually how teams find out. Our engineers write Spark that respects how Spark actually executes: minimising shuffles, partitioning deliberately, and caching only where it pays.

What clients hire our PySpark engineers for

Large-scale ETL

Processing terabyte-scale event, transaction, or log data into a warehouse on a schedule, with jobs that finish inside their window.

Migrating jobs that outgrew pandas

Porting single-machine pipelines to Spark while preserving the business logic and, critically, proving the outputs still match.

Cutting cluster cost

Profiling existing jobs to find the shuffle, skew, or bad partitioning that is burning compute — often the cheapest performance win available.

PySpark skills we staff for

PySpark DataFrame & SQL APIs

Partitioning, shuffles & skew handling

Databricks, EMR & Dataproc

Delta Lake / Parquet optimisation

Streaming with Structured Streaming

Cost and performance profiling

When it’s worth bringing in PySpark expertise

Hire a PySpark engineer when your nightly job no longer finishes by morning, when cluster costs are rising faster than data volume, or when you are moving off single-machine processing and want the migration verified rather than assumed.

Flexible Hiring Models

Hourly

Pay only for the hours you need. Ideal for short tasks, fixes, and overflow work.

Dedicated (Monthly)

A full-time developer working only on your product, integrated into your team and tools.

Dedicated Team

A full squad — developers, QA, and a project manager — to own your roadmap end to end.

Need a whole team instead of one engineer? IT staff augmentation or browse every role we staff.

Hiring PySpark developers — FAQs

Discuss your project

Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.

Your enquiry goes straight to our engineering team — the people who will scope and build your project, not a sales desk.

sales@dablewdevs.com Book a free 30-min call