Quality Engineer-Data
IBM Mumbai Metropolitan Region
Job Description
"Unlock the secrets to a high-flying career as a Quality Engineer-Data at IBM, where curiosity fuels success and innovation drives results."
In this role, you'll be at the forefront of shaping client journeys, leveraging IBM's cutting-edge technology and expertise to drive meaningful change and accelerate impact.
As a Quality Engineer-Data, you'll play a critical part in ensuring the accuracy and reliability of data, working closely with cross-functional teams to develop and implement comprehensive testing strategies.
Why you should learn this:
With the increasing demand for data-driven insights, the role of Quality Engineer-Data is in high demand, with a median salary range of $115,000 - $170,000 per year.
Expected Salary: According to Indeed, Glassdoor, and LinkedIn, the average salary for a Quality Engineer-Data in the United States is around $142,000 per year.
How it works:
- Develop and execute comprehensive data quality testing strategies to ensure the accuracy and reliability of data.
- Collaborate with cross-functional teams, including data engineers, data scientists, and software developers, to design and implement data quality solutions.
Core Concepts to Master
Data Quality Frameworks
A data quality framework is a structured approach to ensuring the accuracy, completeness, and consistency of data. It typically includes data profiling, data cleansing, data transformation, and data validation.
Data Testing Strategies
Data testing strategies involve designing and executing tests to validate data quality. This includes testing data for accuracy, completeness, and consistency, as well as testing data for adherence to business rules and data standards.
Data Validation Techniques
Data validation techniques involve verifying that data conforms to expected formats, values, and relationships. This includes using data validation rules, data profiling, and data quality metrics to ensure data quality.
Interview Questions (Beginner)
- What is data quality, and why is it important?
- Can you describe a time when you had to troubleshoot a data quality issue?
- How do you stay up-to-date with new data quality tools and technologies?
Job Overview
Advance Questions
- • Design a comprehensive data quality testing strategy for a given dataset. How would you ensure data accuracy, completeness, and consistency?
- • How do you balance the need for data quality with the need for data velocity and agility?
- • Can you describe a scenario where you had to implement data quality controls to prevent data breaches or security incidents?