Hire big data engineers whose pipelines stay correct at volumes your laptop can't simulate.
Ingestion, streaming, and warehouse infrastructure, built by engineers who design for backpressure, schema drift, and late-arriving data, the failure modes that only show up once real production volume hits the pipeline.
Batch and streaming ingestion built to handle backpressure and failure without silently dropping records.
Warehouses and lakes structured for the query patterns your analysts and models actually use.
Versioned schemas and migration paths that don't break every downstream consumer.
Data quality checks and pipeline alerting so a broken feed surfaces immediately, not weeks later.
What strong Big Data Engineers know cold.
from $24/hr
A typical range. Your final rate depends on experience level, timezone overlap and how long you book for.
Four stages. Three percent get through.
- 01Screening38%Résumé, background, and communication check, the fastest way to rule out a bad fit.
- 02Technical Deep Dive22%A senior engineer probes real system design and stack depth, not trivia.
- 03Live Build Test9%A timed, real-world task. We watch how they actually ship, not just what they claim.
- 04Client Fit Interview3%Ownership, reliability, and how they work inside your team on day one.
- Shortlist in 48 hoursProfiles, not a waiting list.
- Free replacementWrong fit swapped at no cost.
- No conversion feeHire them direct whenever you want.
- IP yours from day oneAssigned in writing, not at handover.
- Month to monthNo lock-in, and no notice period.
Shipped, not slideware.
On camera, in their own words.
Why he brought his development work to Code Elevator.
Hiring for something adjacent?
Answered before you ask.
A big data engineer builds and operates the pipelines and infrastructure that move and store data reliably at scale; a data scientist analyzes that data to answer specific questions. Most engagements that need both staff them as separate, collaborating roles.
Once ingestion or processing regularly runs into the tens of millions of records, or streaming throughput becomes a real constraint, the tooling and design patterns diverge enough that dedicated big data experience matters.
Yes. Most engagements build directly against your existing Snowflake, BigQuery, or Redshift setup rather than proposing a parallel system.
Versioned schemas with a defined migration and deprecation window, plus contract tests against downstream consumers, so a change is caught in CI rather than in a broken dashboard three teams away.
Every way out of this hire is already written down.
Not a fit? Replace anyone in the first two weeks. No questions asked, no replacement fee. We re-match from the same vetted pool.
You pay only for engineers who actually start. Seeing candidates costs nothing.
Dedicated and managed engagements run on a monthly rolling contract. Cancel with notice.
You see the number before you commit. Nothing is added on top of it later.
Bring us the pipeline. Three matched engineers in 48 hours.
A feed that's starting to buckle, or a warehouse that needs a real ingestion layer. We'll scope it against your actual volume, not a sample.
We reply within an hour during our working day in India and the UAE.