QA Engineer

Summary

Join Zyte's Data QA team as a Data Scientist and leverage your data analysis skills to ensure the quality and usability of web-scraped data. You will translate customer requirements into test approaches, utilizing automated and semi-automated techniques for data wrangling, manipulation, and visualization. Responsibilities include owning the end-to-end QA process for new projects, collaborating with various stakeholders, and proactively suggesting improvements to QA methodologies. You will draw conclusions about data quality through statistical analysis and visualizations, presenting findings to stakeholders. The role requires proficiency in programming languages like Pandas, SQL, or R, a background in data profiling, and strong analytical skills. Zyte offers a globally distributed team environment, flexible work arrangements, and opportunities to work with cutting-edge technologies.

Requirements

• Highly proficient in one or more of Pandas, SQL, R, Excel
• BS degree in Computer Science, Engineering, Mathematics, Statistics or equivalent
• Demonstrable programming knowledge and experience, minimum of 3 years (please provide code samples in your application - ideally pertaining to data analysis - via a link to GitHub or other publicly-accessible service)
• Background in data profiling
• Strong analytical skills with unstructured data
• Experience in data management, data integration and data quality verification
• Experience in data quality visualization and the visualisation of data quality issues
• Ability to work with very large datasets (into the millions of records)
• Strong knowledge of software QA methodologies, tools, and processes
• Excellent level of written and spoken English; confident communicator; able to communicate on both technical and non-technical levels with various stakeholders on all matters of QA
• Outstanding attention to detail and ability to meet deadlines

Responsibilities

• Understand customer web scraping and data requirements; translate these into test approaches that include exploratory manual/visual testing and any additional automated tests deemed appropriate
• Provide input to our existing test automation frameworks from points of view of test coverage, performance, etc
• Ensure that project requirements are testable; work with project managers and/or clients to clarify ambiguities before QA begins
• Take ownership of the end-to-end QA process in newly-started projects
• Work under minimal supervision and collaborate effectively with Head of QA, Project Managers, and Developers to realize your QA deliverables
• Draw conclusions about data quality by producing basic descriptive statistics, summaries, and visualisations
• Proactively suggest and take ownership of improvements to QA processes and methodologies by employing other technologies and tools, including but not limited to: browser add-ons, Excel add-ons, UI-based test automation tools etc
• Understand customer web scraping and data requirements; map these requirements to custom scripts in your language/tool of choice, with a view to establishing the degree of data quality and uncovering data quality issues
• Draw conclusions about data quality by producing descriptive and inferential statistics, summaries, and visualisations
• Supplement existing manual QA and schema validation techniques with advanced data wrangling and manipulation
• As needed, perform complementary manual and semi-automated verification
• Collaborate with developers to further troubleshoot and pinpoint solutions
• Present findings and conclusions to stakeholders at various levels (other members of the QA department, developers, project managers, account managers, customers)
• Write high-quality, well-structured code that is maintainable and extensible
• Manage code using GitHub, BitBucket and other version control approaches as applicable

Preferred Qualifications

• Prior experience in a Data QA role (where the focus was on verifying data quality, rather than testing application functionality)
• Familiarity with Jupyter and JupyterLab
• Experience with dashboard and monitoring tools such as Grafana, Kibana, FineReport etc
• Experience building your own dashboards
• Interest in and flair for Data Science concepts as they pertain to data analysis and data validation (machine learning, inferential statistics etc.); if you have ideas, mention them in your application
• Experience with Spark, BigQuery, and other big data technologies
• Knowledge of and experience in other technologies that support a modern cloud-based software service (Linux, AWS, Docker, Kafka etc.)
• Previous remote working experience

Benefits

• Become part of a self-motivated, progressive, multi-cultural team
• Have the freedom and flexibility to work from where you do your best work
• Attend conferences and meet with team members from across the globe
• Work with cutting-edge open source technologies and tools