QA Data Lead

At Cobra Studio, we are seeking a QA Data Lead to drive quality strategies in data-related projects, collaborating with cross-functional teams to ensure robust and scalable solutions. This role requires expertise in validating complex systems, test automation, and implementing quality processes throughout the software development lifecycle (SDLC). The required english level is B2. This is a full-time remote position with compensation in US dollars.

QA
Python
SQL
ETL
ELT
DBT
CI/CD
Azure Devops
Terraform
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JOB DESCRIPTION:

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Benefits and conditions

  • Fully remote job.
  • All required equipment will be provided.
  • Dynamic and interesting work with lots of growth opportunities.
  • Work alongside a high-performing technical team.

Requirements

  • Bachelor’s degree in Systems, Networks, or related fields.
  • At least 5 years in QA roles focused on data systems.
  • Proficiency in Python and PyTest.
  • Strong knowledge of SQL, ETL/ELT processes, and data warehouses/data marts.
  • Expertise in data validation and automated testing frameworks.

Nice to have

  • Experience with DBT (Data Build Tool) and Databricks.
  • Familiarity with cloud platforms and Terraform for infrastructure as code.
  • Proficiency in Azure DevOps for CI/CD processes.
  • Domain knowledge of the insurance industry.

Soft skills

  • Strong leadership and team mentorship capabilities.
  • Excellent communication and collaboration skills.
  • Analytical mindset with a proactive approach to problem-solving.
  • Commitment to fostering a culture of quality and continuous improvement.

Responsibilities

Leadership and Strategy:

  • Define and implement testing strategies across multiple teams.
  • Advocate for quality from the early stages of development (shift-left testing).
  • Mentor SDETs and promote best practices within the team.
  • Adopt methodologies like BDD and TDD to enhance quality and efficiency.

Automation and Validation:

  • Design and execute automated test scripts using PyTest for functional, integration, and regression testing.
  • Validate data systems, transformation processes (ETL/ELT), and data models.
  • Manage test cases and defect tracking using tools like Gherkin.

Collaboration and Continuous Improvement:

  • Partner with developers, analysts, and data engineers to improve data quality.
  • Propose innovative solutions to address data integrity and consistency challenges.

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