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For Companies At a Glance
- Tasks: Join us as a QA Engineer to ensure data quality in our innovative insurance tech.
- Company: We're a fast-growing company revolutionising the insurance market since 2018.
- Benefits: Enjoy remote work, a collaborative culture, and opportunities for professional growth.
- Why this job: Be part of a dynamic team that values quality and innovation in data-driven decisions.
- Qualifications: 5+ years in software testing with strong SQL skills and programming experience.
- Other info: Work closely with data engineers and advocate for best practices in a supportive environment.
The predicted salary is between 36000 - 60000 £ per year.
The Company
Founded in 2018, our client are revolutionising the insurance market and have grown year on year. They operate a remote set-up and the overall business is made up of self-managed and collaborative high performers.
About the Role
We’re looking for a QA Engineer with strong data expertise to join our client in the Insurance Technology space. This isn’t a typical QA role — you’ll work closely with data and analytics engineers to ensure the quality, accuracy, and trustworthiness of data products that power critical business decisions. You’ll be instrumental in designing robust testing frameworks, advocating for data quality best practices, and enabling teams to move fast without compromising on integrity.
Key Responsibilities
- Define and execute test strategies for data pipelines to ensure data accuracy, reliability, and usability.
- Collaborate with data engineers, analytics engineers, and product stakeholders to validate requirements and guide test coverage.
- Design, develop, and maintain automated tests that embed quality into the heart of the development lifecycle.
- Identify potential risks and data regression scenarios based on downstream consumption patterns.
- Balance speed and quality in a dynamic, agile data environment by targeting meaningful checkpoints.
- Champion a culture of quality and advocate for testing best practices across data teams.
Required Skills & Experience
- 5+ years of experience in software testing, with a strong emphasis on data quality and pipelines.
- Advanced proficiency in SQL and relational database systems.
- Programming experience in one or more of the following: Python, JavaScript, TypeScript, Bash/Shell.
- Excellent analytical and problem-solving abilities, with a strong attention to detail.
- Proven experience collaborating with cross-functional teams to drive data quality outcomes.
- Familiarity with modern data stacks (Snowflake, Redshift, Databricks) and data testing principles.
Quality Assurance Engineer (Data Platform) employer: Annapurna
Contact Detail:
Annapurna Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quality Assurance Engineer (Data Platform)
✨Tip Number 1
Familiarise yourself with the specific data technologies mentioned in the job description, such as Snowflake and Redshift. Having hands-on experience or even completing relevant online courses can give you a significant edge during discussions.
✨Tip Number 2
Network with current or former employees in similar roles within the insurance technology space. Engaging in conversations about their experiences can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your previous projects that involved data quality and testing frameworks. Be ready to share specific examples of how you identified risks and implemented solutions, as this will demonstrate your expertise and problem-solving skills.
✨Tip Number 4
Showcase your collaborative skills by highlighting any cross-functional team experiences in your conversations. Emphasising your ability to work closely with data engineers and product stakeholders will align well with the company's culture of self-managed collaboration.
We think you need these skills to ace Quality Assurance Engineer (Data Platform)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in software testing, particularly focusing on data quality and pipelines. Include specific examples of projects where you've collaborated with data engineers or analytics teams.
Craft a Compelling Cover Letter: In your cover letter, emphasise your passion for data quality and your ability to design robust testing frameworks. Mention any relevant tools or technologies you’ve worked with, such as SQL or modern data stacks like Snowflake or Redshift.
Showcase Relevant Skills: Clearly outline your programming skills in languages like Python or JavaScript, and provide examples of how you've used these in previous roles. Highlight your analytical abilities and attention to detail, as these are crucial for the role.
Demonstrate Collaboration Experience: Provide examples of how you've successfully collaborated with cross-functional teams. This could include working with product stakeholders or other engineers to drive data quality outcomes, which is essential for this position.
How to prepare for a job interview at Annapurna
✨Showcase Your Data Expertise
Make sure to highlight your experience with data quality and pipelines during the interview. Be prepared to discuss specific projects where you ensured data accuracy and reliability, as this role heavily focuses on these aspects.
✨Demonstrate Your Technical Skills
Since advanced proficiency in SQL and programming languages like Python or JavaScript is crucial, be ready to answer technical questions or even solve coding challenges. Brush up on your skills and consider doing some practice tests beforehand.
✨Collaborate and Communicate
This role requires working closely with cross-functional teams. Prepare examples of how you've successfully collaborated with data engineers or product stakeholders in the past. Emphasise your communication skills and ability to advocate for data quality best practices.
✨Understand the Company’s Culture
Research the company’s remote setup and self-managed team structure. Be ready to discuss how you thrive in a collaborative environment and how you can contribute to their culture of quality and agility in data testing.
