Senior Data Warehouse Engineer II

As a Senior Data Warehouse Engineer II at Careem, your responsibilities include designing and developing robust data pipelines, collaborating with various stakeholders to understand their data needs, optimizing ETL processes, and ensuring data quality and integrity across our systems. You’ll play a vital role in supporting data-driven decision-making and providing critical data infrastructure for analytics.

To be considered for the Senior Data Warehouse Engineer II position at Careem, you should have a Bachelor's degree in Computer Science or a related field, along with 7+ years of relevant experience in data engineering. Strong programming skills in languages like Python or Java, proficiency with SQL and database technologies, and familiarity with big data tools are crucial for success in this role.

Careem is committed to fostering professional growth for our team members. As a Senior Data Warehouse Engineer II, you will have opportunities to learn from experienced colleagues, engage in continuous learning, and explore innovative solutions in data engineering. We also encourage participation in relevant training and development programs.

At Careem, we offer a flexible and engaging working environment. As a Senior Data Warehouse Engineer II, you can enjoy a hybrid work model with four office days and one remote day a week, and even work from anywhere in the world for up to 30 days a year. This flexibility promotes a healthy work-life balance.

In the role of Senior Data Warehouse Engineer II at Careem, you will work with various tools and technologies including Apache Spark for data manipulation, SQL for database interactions, and big data technologies like Hadoop and Hive. Familiarity with cloud platforms like AWS or Azure and workflow management tools like Apache Airflow will also be advantageous.

When answering this question, discuss specific ETL projects you worked on. Share details on the tools used, the data sources, and how you ensured the efficiency and accuracy of the data pipelines. Highlight any challenges you faced and how you overcame them.

In your response, outline your methods for maintaining data quality such as implementing data validation techniques, monitoring data integrity, and conducting regular audits on data pipelines. Discuss any tools or frameworks you’ve utilized to achieve high standards in data quality.

Explain how you engage with the data engineering community, attend relevant workshops, read industry blogs, or participate in online courses. This demonstrates your commitment to professional development and innovation in your work as a Senior Data Warehouse Engineer II.

Detail a specific scenario where you encountered a complicated data issue. Explain the steps you took to analyze the problem, the strategies employed to resolve it, and the positive outcome that resulted. This showcases your analytical skills and problem-solving abilities.

Discuss techniques such as partitioning, indexing, and using appropriate data structures. Mention specific tools or frameworks you've worked with that help optimize performance in data processing tasks.

When answering this question, elaborate on your experience with platforms like AWS, Azure, or GCP. Share specific projects or tools you’ve used on these platforms to implement data solutions, highlighting your familiarity with cloud-based data warehousing.

Talk about your communication style and how you effectively engage with different stakeholders. Provide examples of successful collaborations where your contributions as a Senior Data Warehouse Engineer II significantly impacted the project outcomes.

Cite specific data warehousing solutions you have experience with, such as Snowflake, Amazon Redshift, or Google BigQuery. Discuss your role in the implementation and any challenges you faced, along with how you resolved them.

Describe your approach to documentation, emphasizing the importance of clarity and accessibility for your team members. Share the tools or methods you prefer for keeping documentation up to date.

Discuss your proficiency in programming languages relevant to data engineering, such as Python or Java. Provide specific examples of how you’ve used these languages in data manipulation, processing, or developing ETL pipelines.