Opportunities for Growth: Walmart offers numerous opportunities for career advancement. Employees can explore various roles and departments, allowing for professional growth within the company.
Comprehensive Benefits: Walmart provides a competitive benefits package, including health insurance, 401(k) plans, and employee discounts, which enhance overall job satisfaction.
Innovative Projects: Working at Walmart, especially in data engineering roles, allows you to work on cutting-edge projects that leverage advanced technologies and data analytics.
Diverse Work Environment: Walmart is known for its inclusive culture and diversity in the workplace, which fosters a collaborative and supportive environment.
Stability: As one of the largest retailers in the world, Walmart offers job stability and security.
Work-Life Balance: Some employees report challenges in maintaining a work-life balance, especially during peak seasons or when working on critical projects.
Bureaucracy: The size and complexity of the organization can sometimes lead to bureaucratic hurdles, making it difficult to implement changes or new ideas quickly.
High Expectations: The fast-paced environment and high expectations for performance can be stressful for some employees, leading to burnout if not managed properly.
Limited Flexibility: Depending on the role, there may be limited flexibility in terms of remote work or flexible hours, which can be a drawback for those seeking more work-life balance.
Communication: In such a large organization, effective communication can sometimes be a challenge, with information not always being disseminated promptly or clearly.
The interview was not very hard, but they definitely look for basics in Spark, hands-on experience, coding in Python, and SQL. There were two technical rounds, mainly based on Scala, Spark, PySpark, and SQL.
There will be enormous testing on your data manipulation techniques. How well you can understand the requirements and how well you can securely store the data pipelines will be assessed.
First screening round. I was prepared for a DS/Algo round, but the questions were data engineering-focused. I panicked and could not perform well. They went into too much detail about data engineering, the internals of how frameworks work, and other
The interview was not very hard, but they definitely look for basics in Spark, hands-on experience, coding in Python, and SQL. There were two technical rounds, mainly based on Scala, Spark, PySpark, and SQL.
There will be enormous testing on your data manipulation techniques. How well you can understand the requirements and how well you can securely store the data pipelines will be assessed.
First screening round. I was prepared for a DS/Algo round, but the questions were data engineering-focused. I panicked and could not perform well. They went into too much detail about data engineering, the internals of how frameworks work, and other