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AI Metadata Quality Scoring Engineer

Lily Santos

Full-time · Job · London

About the role

A data catalog is only as good as its metadata. If table descriptions are vague, column names are cryptic, and ownership is unassigned, the catalog becomes a graveyard that nobody searches. Our metadata quality scorer evaluates every asset in the catalog across six dimensions: completeness, accuracy, freshness, discoverability, documentation quality, and usage patterns. The scoring system uses a combination of rule-based checks (does this table have a description? Is the owner still active?) and ML-based assessments (is this description actually useful or just "this is a table"? Does the column name match the data type and distribution?). You'll own the scoring engine, designing new quality signals, calibrating scores against user satisfaction surveys, and building the nudge system that prompts data owners to improve their worst-scoring assets. The data governance team is five engineers. You'll be the senior engineer who defines what "quality" means for metadata and builds the system that measures it. Your scores appear on every asset page in the catalog and drive the gamification features that motivate data teams to maintain their metadata.

Responsibilities

  • Design metadata quality scoring across completeness, accuracy, and usability dimensions
  • Build ML-based assessment of description quality and naming conventions
  • Calibrate quality scores against user satisfaction and search success metrics
  • Create nudge systems prompting data owners to improve low-scoring assets
  • Define quality standards and thresholds for the data catalog platform
  • Evaluate scoring effectiveness through A/B testing of nudge strategies

Requirements

  • 5+ years in data engineering or data governance with quality focus
  • Experience building scoring systems or recommendation engines
  • Strong SQL and Python skills with statistical analysis capability
  • Understanding of data catalog and metadata management concepts
  • Ability to define and operationalize abstract quality concepts

Benefits

  • Fully remote across UK/EU time zones
  • 30 days PTO
  • Private health insurance
  • £3,500 annual conference budget
  • Shape metadata quality standards for 85 enterprise clients

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Categories

data science & analytics AI Product & Strategy

Skills

python sql spark statistics docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

data science & analytics AI Product & Strategy

Skills

python sql spark statistics docker fastapi

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