Expert QA Consultant

The client is a consultancy firm specializing in marketing transformation and digital media, focused on helping businesses make data-driven decisions through expertise in Customer Data Platforms (CDPs), data management, and marketing automation. They integrate AI, advanced analytics, and automation to guide organizations through digital transformation and optimize data management and decision-making. Jit Team employs over 770 experienced experts and supports charitable and educational activities, offering developmental projects and professional development programmes.

• Extensive experience as a QA Lead, QA Consultant, Test Lead, or Quality Engineering Lead
• Strong hands-on experience with Python
• Experience with Databricks
• Advanced knowledge of SQL
• Experience working in Big Data environments
• Experience with PySpark
• Experience with PyTest
• Proven ability to define and execute test strategies for complex projects
• Strong stakeholder management and communication skills
• Experience leading QA teams and mentoring engineers
• Ability to combine strategic thinking with hands-on delivery
• Fluent English communication skills

Nice to have:

• Experience testing AI-powered solutions and automation initiatives
• Experience working with LLM-based projects
• Knowledge of SaaS integrations and third-party APIs
• Experience with Customer Data Platforms (CDPs) or marketing technology ecosystems
• Cloud platform experience

• Lead and drive all QA activities across data and integration platforms
• Define, implement, and continuously improve the overall test strategy
• Build and coordinate the testing environment across the full QA lifecycle
• Manage testing efforts within complex data-driven initiatives
• Work closely with stakeholders, engineering teams, product teams, and client representatives
• Provide technical guidance and coaching to QA engineers and testers
• Establish best practices for data validation, test automation, and quality assurance processes
• Identify project risks and propose effective mitigation strategies
• Support testing activities related to data integrations, APIs, and Big Data platforms
• Present testing recommendations, trade-offs, and quality considerations to stakeholders
• Drive continuous improvement through automation, AI-driven testing, and modern QA practices