Senior Data Warehouse Engineer ll

The Senior Data Warehouse Engineer II at Careem is primarily responsible for designing and developing high-performance data pipelines and ETL processes to manage large-scale data. This role involves collaborating with cross-functional teams to understand their data needs, implementing data governance practices, optimizing data flows, monitoring data quality, and providing the necessary infrastructure for data analysis.

To qualify for the Senior Data Warehouse Engineer II position at Careem, candidates must hold a Bachelor’s degree in Computer Science, Engineering, or a related field. Proven experience in data engineering, strong programming skills in languages like Python or Java, familiarity with big data technologies such as Hadoop and Hive, as well as excellent problem-solving skills are essential for this role.

The Senior Data Warehouse Engineer II at Careem will work with a variety of technologies including SQL for data manipulation, Apache Spark for processing large data sets, and cloud platforms such as AWS, Azure, or GCP. Familiarity with tools like Apache Airflow and knowledge of data warehousing solutions are also crucial to effectively manage data workflows.

Careem is committed to employee growth, offering opportunities to drive impact and explore new skills daily. As a Senior Data Warehouse Engineer II, you'll be part of a community of inspiring colleagues and partake in a unique work culture that encourages learning, collaboration, and innovation within a rapidly evolving industry.

At Careem, the work-life balance is prioritized, providing opportunities to work four days in the office and one day from home. Additionally, employees can work remotely for 30 days a year and enjoy unlimited vacation. This flexibility allows the Senior Data Warehouse Engineer II to maintain personal and professional commitments effectively.

The ETL process, which stands for Extract, Transform, Load, is vital for a Senior Data Warehouse Engineer II as it involves taking data from various sources, transforming it into a usable format, and loading it into a data warehouse. This ensures that data is accessible for analysis, supporting informed decision-making across the organization.

To optimize an existing data pipeline, I would analyze its performance metrics and identify bottlenecks. Implementing parallel processing, refining algorithms for data transformation, and utilizing efficient data storage solutions can significantly speed up the pipeline, improving both performance and reliability.

In a previous project, I encountered a significant data integrity issue where discrepancies in data entered into the warehouse caused analytical errors. I addressed this by implementing rigorous validation rules during the ETL process, creating automated checks, and re-training users on correct data entry practices, ultimately ensuring consistent and accurate data.

I am well-versed in big data technologies including Apache Hadoop and Apache Spark. I have used Spark for real-time data processing tasks, leveraging its speed and versatility to analyze large data sets efficiently, which contributed to quicker insights and better data-driven decisions.

To ensure compliance, I implement strict data governance policies and regularly audit our data systems for security vulnerabilities. Keeping abreast of regulatory changes and providing training for teams on compliance best practices are also essential to safeguard sensitive information.

Documentation is crucial in data engineering as it maintains clarity on data workflows, ETL processes, and system architectures for current and future team members. I ensure to document each step thoroughly, fostering a culture of knowledge sharing and enabling efficient onboarding of new team members.

I have extensive experience with cloud platforms like AWS and Azure. I’ve deployed data solutions leveraging services such as AWS S3 for storage and Redshift for data warehousing, which facilitated scalable and cost-effective solutions aligned with business objectives.

I keep updated on the latest trends in data engineering by attending industry conferences, enrolling in online courses, and following influential data engineering blogs and forums. Networking with peers also provides insights into emerging trends and technologies.

I prioritize clear communication and understanding each team's objectives when working within cross-functional teams. Establishing regular check-ins, using collaborative tools, and being open to feedback are all strategies I implement to facilitate seamless collaboration and ensure project alignment.

I am drawn to Careem for its innovative approach to transforming the Middle East with technology. The opportunity to contribute to a purpose-driven organization that prioritizes talent development and showcases a commitment to community impact aligns perfectly with my professional and personal values.