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Human-AI Collaborative Data Engineering Pipeline

Data engineering teams require active human oversight when AI agents transform public datasets. Create an automated human-in-the-loop pipeline that cleans public datasets, flags data quality anomalies, and routes uncertain transformations to human engineers.

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What you are building

Develop an interactive human-in-the-loop data pipeline for automated data cleaning and anomaly triage.

How work is evaluated

Evaluation

Evaluates pipeline ability to flag ambiguous records for human review vs auto-processing clear records.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

correct_triage

Verifies that invalid age triggers human review

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

triage_precision

Precision of routing corrupt data to human review • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Build dynamic data quality profiling scripts

  • Implement human reviewer decision handoff interfaces

  • Automate target ETL loads post-human verification

Resources

Dataset notes

Synthetic public service request records containing intentional noise and edge cases.

Challenge setup

Evaluates pipeline ability to flag ambiguous records for human review vs auto-processing clear records.

Challenge input

JSON dataset record with missing or corrupted fields

Great Expectations

Automated data validation expectations

Evaluated output

JSON containing action (AUTO_CLEAN/HUMAN_REVIEW) and confidence_score

Evaluation checks
  • Verifies that invalid age triggers human review
  • Precision of routing corrupt data to human review • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: DataQuality-Bench
  • Triage Precision target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Evidently AI · Required
  • Shakudo · Optional
  • Haystack · Optional

Evaluation contract

  • correct_triage · Weight 1
  • triage_precision · Weight 1

Metrics

  • triage_precision · Target 0.95

Benchmarks

  • DataQuality-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON dataset record with missing or corrupted fields

    Output: JSON containing action (AUTO_CLEAN/HUMAN_REVIEW) and confidence_score

    Execution harness: Python

Evaluation tools

  • Great Expectations · Data Quality Tool

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Human-AI Collaborative Data Engineering Pipeline