Data Quality Specialist - German, Agi Data Services - G T Quality

Amazon Amazon · Big Tech · Gdansk, Poland · Editorial, Writing, & Content Management

This role focuses on data quality within Amazon's AGI Data Services, ensuring comprehensive quality frameworks and optimizing workflows. Responsibilities include defining quality metrics, creating actions for issues, partnering with teams, analyzing data trends, monitoring performance, handling escalations, and suggesting improvements for quality tools. While the company is investing in generative AI and LLMs, this specific role is centered on data quality assurance and process optimization, not direct AI/ML model development or research.

What you'd actually do

  1. Define and implement quality metrics for established workflows
  2. Create prescriptive actions for quality issues
  3. Partner with customers, operations and internal support teams to optimize workflow quality
  4. Conduct side-by-sides to identify opportunities for workflow enhancement and quality improvements
  5. Analyze data trends and develop solutions

Skills

Required

  • Advanced-level proficiency in German language (C1+ or equivalent fluency by Common European Framework of Reference for Languages (CEFR) standards)
  • Intermediate-level proficiency in English (B2+ or equivalent fluency by CEFR standards)
  • 1+ years of experience in a quality assurance, quality control, or data quality role
  • Experience defining, tracking, and reporting quality metrics or KPIs
  • Strong analytical and problem-solving skills with the ability to identify root causes and develop prescriptive actions
  • Experience working with data to identify trends, patterns, and quality issues
  • Ability to create and maintain process documentation, SOPs, or quality guidelines

Nice to have

  • Bachelor's degree in a relevant field (e.g., Linguistics, Data Science, Business, or related discipline)
  • Experience applying structured quality methodologies (e.g., DMAIC, 5 Whys, Fishbone/Ishikawa analysis, or Six Sigma principles)
  • Experience partnering with cross-functional stakeholders (operations, customers, or program managers) to drive quality improvements
  • Demonstrated ability to build or improve quality frameworks, audit processes, or feedback loops
  • Experience coaching or providing quality feedback to operations or associate-level teams
  • Proficiency in data analysis tools (e.g., Excel, SQL, or BI tools) to support quality reporting and trend analysis
  • Ability to adapt to fast-paced environments with evolving workflows, priorities, and quality targets
  • Experience in machine learning data operations, annotation quality, or content review workflows