What the work actually looks like

The job title says Big Data Engineer, and the listing reads like a standard pipeline role, but the summary line is the honest one: you're training next-generation AI systems. In practice that means writing realistic data engineering problems and reference solutions, grading model output against them, and explaining in writing why an answer is wrong. Typical tasks include authoring a multi-stage Spark or Flink transformation with a known correct result, reviewing a model's PySpark job for silent correctness failures (skewed joins, wrong watermarking, nondeterministic dedup), comparing two model answers on schema design or partitioning strategy, and writing the rationale that a grader or another model can learn from. Some engagements include genuine build work — pipelines, ingestion, orchestration — used as source material; most of the volume is authoring and evaluation.

What the screen is looking for

micro1's screening is AI-led and heavily follow-up driven. It will take a claim on your CV — "built pipelines processing billions of events" — and drill into volumes, cluster sizing, file formats, partition keys, and what broke. Expect to be asked to reason aloud about a query plan or a shuffle problem without an IDE in front of you. The screen also probes evaluation judgment: whether you can distinguish code that is wrong from code that is merely not how you'd write it, and whether you can write a critique that is specific enough to act on. Vague seniority signals do poorly here; concrete numbers, named tools, and admitted trade-offs do well.

Logistics

  • Fully remote, contractor status, no employment relationship implied.
  • Largely asynchronous — work is claimed from a queue with turnaround windows rather than fixed shifts.
  • Volume is variable. Many contributors treat this as 10–20 hours a week alongside other work; some engagements ramp to near full-time for a stretch.
  • Rates are observed in the $30–80/hr range and are set by assessed skill band and task type, not negotiated freely. Nothing about a band is guaranteed before you pass the screen.
  • Written English matters more than in a normal engineering role, because your written explanation is often the deliverable.