Fetcherr

QA Engineer

4 December 2024
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Deadline date:
£68000 - £127000 / year

Job Description

Description

QA Engineer (PO)

Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

We’re looking for a QA Engineer to help us grow our data quality assurance and automation team’s capabilities. The ideal candidate is an AQA professional with data intensive applications experience who is capable of building data quality assurance strategy for a team of 20+ developers from scratch.

You will ensure the quality of our data products, and create automated testing, monitoring and alerting infrastructure for high-load data pipelines involving multiple midsize teams.

Requirements

You’ll be a great fit if…

  • You know how to develop and execute the team’s QA strategy
  • You can organize and train AQA engineers, ensuring knowledge sharing and continuous improvement
  • You have experience setting up automated control and reporting processes
  • You have working in tech lead position or equivalent for 2+ years
  • You are able to create and analyze company quality metrics
  • You are familiar with testing methodologies and forms such as GreyBox, WhiteBox, API, Performance, Stress Testing, Exploratory Testing, etc.
  • You are proficient in utilizing CI/CD, containerization and cloud technologies
  • You are fluent with Python testing toolkit
  • BSc or Master’s degree in Economy / Computer Science / Engineering
  • Have a readiness and willingness to continuously learn while working

Nice to have:

  • Obsession for testing ETL pipelines and orchestration workflows
  • Experience with Python libraries like pandas, numpy, matplotlib, plotly
  • Familiarity with data quality assurance tools, e.g. Great Expectations, DBT, SODA, DVC, etc.
  • Domain knowledge in revenue management for the travel industry (airlines, transportation, hospitality)