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AI Schema Evolution Detection Engineer

Benjamin Leclerc

Full-time · Job · Berlin

About the role

Data pipelines break silently when upstream schemas change. A column gets renamed, a data type shifts from integer to string, a new nullable field appears, or an enum gains a new value — and suddenly your downstream models produce garbage without any error. Our schema evolution detector catches these changes before they propagate. You'll build the monitoring layer that continuously profiles incoming data against expected schemas: comparing column distributions, detecting type mismatches, flagging new or missing fields, and alerting data teams before broken data reaches their models. The challenge is distinguishing real schema changes from normal data variation — a new category value might be a schema evolution or just a rare but valid data point. The data quality team is four engineers. You'll start by building the profiling module that computes schema fingerprints from data samples. Your senior teammate handles the alerting and notification layer; you'll feed clean change signals into their system. Expect to learn a lot about data quality patterns across 80 client data pipelines.

Responsibilities

  • Build schema profiling modules that fingerprint incoming data characteristics
  • Detect column additions, removals, type changes, and distribution shifts
  • Distinguish real schema changes from normal data variation
  • Implement monitoring for 80+ client data pipelines
  • Write tests for change detection accuracy and false positive rates
  • Document schema evolution patterns and detection rules

Requirements

  • 1+ year of Python development with SQL experience
  • Basic understanding of data pipeline concepts and data quality
  • Familiarity with data profiling and statistical distribution comparison
  • Interest in data infrastructure and reliability engineering
  • Systematic approach to testing and edge case handling

Benefits

  • Fully remote across EU time zones
  • 28 days PTO
  • €1,500 learning budget
  • Structured mentorship from senior data engineers
  • Work on a foundational data quality problem

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Categories

data science & analytics MLOps & AI Infrastructure

Skills

python sql spark docker aws

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

data science & analytics MLOps & AI Infrastructure

Skills

python sql spark docker aws airflow

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