Senior Data Engineer - Contractor

The Senior Data Engineer at Beyond is responsible for designing and implementing scalable data pipelines, developing robust data ingestion and processing frameworks using technologies like Kafka and Clickhouse, and ensuring optimal performance of data processing solutions. This role also involves collaborating closely with Machine Learning Engineers and Data Analysts to integrate data pipelines with AI/ML models seamlessly.

To qualify for the Senior Data Engineer position at Beyond, candidates should have at least 5 years of experience in data engineering with a strong focus on real-time analytics. A Bachelor's degree in Computer Science, Engineering, or a related field is required, while a Master's degree and relevant certifications in data engineering and cloud technologies are highly desirable.

Candidates for the Senior Data Engineer position at Beyond should be familiar with technologies such as Kafka, Clickhouse, Snowflake, and various cloud computing platforms like AWS and Google Cloud Platform. Proficiency in programming languages and frameworks like Python, SQL, and Spark is also essential.

The Senior Data Engineer role at Beyond plays a critical part in developing a real-time analytics platform, which is vital for AI initiatives. This position involves designing data architectures and collaborating with Machine Learning Engineers to ensure that data pipelines support the integration of AI/ML models effectively.

Beyond fosters a culture of inclusivity and openness, emphasizing the importance of diversity in all its forms. The work environment encourages collaboration, creativity, and innovation, allowing employees to thrive and contribute to dynamic projects in a rapidly changing technological landscape.

When answering, focus on specific projects where you designed and implemented data pipelines. Discuss the technologies you used, the challenges you faced, and how you ensured scalability and efficiency.

Highlight your experience with frameworks like Kafka and Clickhouse. Discuss any methods you used to optimize ingestion processes, such as tuning performance parameters or implementing monitoring tools.

Explain your strategies for monitoring data flows, implementing error-handling mechanisms, and conducting regular audits to ensure data quality and reliability in real-time scenarios.

Discuss your experience working with ML teams, emphasizing communication, understanding their data needs, and how you've integrated data pipelines with their models for effective results.

Share your knowledge of dimensional modeling and data warehousing methodologies, and discuss how these practices apply to real-time data analytics and AI applications.

Mention specific techniques you’ve employed, such as parallel processing, data partitioning, or query optimization, to enhance the performance of data processing tasks.

Choose a challenging project from your experience. Describe the problem, your analytical approach to finding a solution, and the positive outcomes achieved.

Discuss your familiarity with tools like Looker, Tableau, or Power BI. Explain how you’ve used these tools to transform data into actionable insights for stakeholders.

Talk about the cloud platforms you’ve used (e.g., AWS, GCP) and how you utilized their services for data storage, processing, and integration, emphasizing scalability and flexibility.

Reflect on your experience working in agile environments, describing how flexibility and iterative development have improved your workflow and output in data engineering projects.