Senior Data Engineer, Analytics

As a Senior Data Engineer at CoreWeave, your primary responsibilities include developing and maintaining data models like star and snowflake schemas, optimizing data storage formats, and collaborating with analytics teams to ensure data accuracy and efficiency. You'll also design and manage data pipelines using Airflow, handling large datasets with frameworks like Spark or Flink. Your role will significantly contribute to the data-driven decision-making culture at CoreWeave.

To be a successful candidate for the Senior Data Engineer position at CoreWeave, you should possess over 5 years of experience in programming languages such as Python or Scala, advanced SQL skills for writing complex queries, and hands-on expertise with data modeling principles, particularly Kimball methodology. Additionally, familiarity with analytical storage formats such as Iceberg, Parquet, Avro, and experience with MPP databases like Snowflake or BigQuery are essential.

CoreWeave embraces a hybrid workplace model, allowing Senior Data Engineers the flexibility to balance in-office and remote work. While collaboration is key to our culture, we recognize that work-life balance is vital for productivity. Our commitment extends to onboarding, ensuring new remote employees can effectively integrate into our team, while occasional in-person gatherings help maintain strong connections.

As a Senior Data Engineer at CoreWeave, you'll utilize cutting-edge technologies such as Airflow for batch orchestration, work with distributed computing frameworks like Spark or Flink, and optimize storage formats using tools like Iceberg and Parquet. Your day-to-day will also involve interactions with MPP databases such as StarRocks, Snowflake, BigQuery, or Redshift, ensuring you're always on top of the latest advancements in data engineering.

At CoreWeave, the culture is centered around innovation, collaboration, and adaptability. We encourage our team members, especially Senior Data Engineers, to tackle complex problems and thrive in a dynamic environment. You'll be part of an inclusive and supportive team that values data-driven insights and decision-making, creating a workplace where your contributions can lead to significant changes and advancements.

In answering this question, focus on specific projects where you've applied star or snowflake schema designs. Discuss any methods you used to ensure data integrity and efficiency, highlighting your familiarity with Kimball principles.

Be specific about the techniques you use for query optimization, such as indexing, using proper joins, and analyzing execution plans. Provide examples of previous experiences where these techniques significantly improved performance.

Mention the frameworks you've worked with, for instance, Spark or Flink. Discuss how you've applied them in real-world scenarios, including any challenges you've faced and how you resolved them.

Discuss your strategies for maintaining data quality, like implementing validation checks, using automated testing frameworks, and collaborating with data stakeholders to ensure clarity and accuracy in data definitions.

Provide specifics about the challenges you faced while optimizing data storage, including the technology used, such as Iceberg or Parquet, and the impact that optimization had on data retrieval and processing times.

Talk about your experience with tools like Airflow or Luigi, emphasizing how you've designed, built, and monitored data pipelines to ensure they run efficiently and handle failure gracefully.

Explain how you’ve collaborated with analytics or data science teams, emphasizing the importance of understanding their requirements and translating those into data models that accurately reflect business metrics.

Offer insights into specific projects where you used Python or Scala, detailing your expertise in writing scripts for data manipulation, analysis, or pipeline development.

Mention the MPP databases you've worked with, like Snowflake or Redshift, and provide examples of how you optimized performance or addressed scalability issues in past projects.

Discuss your approach to processing large datasets, including any specific methodologies you've employed, tools used for distributed computing, and how you ensured performance and resource management throughout the process.