As a Software Engineer on the Core Engine team at Eventual, your key responsibilities will involve building essential capabilities for our Daft distributed data engine. You will engage in core architectural design and implementation for components like the Planning/Query Optimizer, Execution Engine, and storage solutions. This role also requires improving memory stability and ensuring efficient resource utilization and task scheduling. It's a chance to contribute meaningfully to creating one of the world's best open-source distributed query engines!

Eventual seeks a Software Engineer with a solid foundation in systems programming, ideally with over 3 years of experience in distributed data systems. Candidates should be proficient in languages such as C++, Rust, or similar, and have experience with cloud technologies like AWS S3. Familiarity with data systems and strong fundamentals in workload optimization and scheduling will be valuable in this role to ensure you're equipped to tackle the challenges of modern data workloads.

Eventual believes in a balanced approach to work, combining the benefits of remote collaboration with essential in-person teamwork. For the Software Engineer position, a flexible hybrid model is in place, typically requiring at least three days of in-person attendance at our San Francisco office from Monday to Wednesday. This ensures effective collaboration while maintaining the flexibility needed for individual productivity.

The interview process at Eventual for the Software Engineer, Core Engine role starts with a brief introductory call with one of our co-founders. This is followed by a technical phone screen to assess your abilities. Candidates then participate in a technical interview panel where further assessments and discussions take place. We also encourage candidates to meet with the team to learn more about our goals and culture, making the process enjoyable and insightful.

Eventual stands out as an employer by focusing on building cutting-edge technologies in the fast-evolving data landscape while fostering a collaborative culture. With backing from prominent investors and a team of experts from top tech companies, you'll have the opportunity to work on a meaningful product used by leading AI/ML companies. The environment encourages intellectual curiosity and autonomy, making it ideal for candidates eager to grow and innovate.

In your response, focus on your specific experiences working with technologies such as Hadoop, Spark, or other distributed systems you've encountered. Mention the projects you worked on, emphasizing your role, the challenges you faced, and how you resolved them, showcasing your problem-solving skills and technical expertise.

When answering this, discuss various optimization techniques such as indexing, partitioning, and query rewriting. Provide examples from your past experiences where these methods led to significant performance improvements in a project, highlighting your understanding of database principles.

Talk about strategies you've employed to guarantee fault tolerance, such as retry mechanisms, data replication, and consistent state management. Drawing from specific scenarios where you implemented these strategies will demonstrate your practical knowledge in creating reliable systems.

Mention languages relevant to the Software Engineer, Core Engine role, such as C++, Rust, or Python. Discuss why you prefer these languages, focusing on their strengths in systems programming and how they relate to building high-performance applications, particularly in data systems.

Here, emphasize your familiarity with various modern databases and data lakes, such as Apache Parquet and Delta Lake. Illustrate your experience in integrating these technologies into your projects and how they have improved data handling and querying efficiency.

Provide a structured approach for troubleshooting, starting with identifying performance bottlenecks using monitoring tools. Discuss how you would analyze logs and metrics, then test solutions iteratively while measuring the impact, showing your systematic problem-solving skills.

Outline how cloud platforms support scalability, flexibility, and resource optimization in data engineering. Provide examples of cloud services you've utilized, like AWS S3 or Azure Data Lake, and how they facilitated your projects or improved your workflows.

Discuss the Execution Engine's role in managing the query execution phase, focusing on aspects such as resource allocation and execution strategy. Provide insights into why efficiency in this component is crucial for overall system performance and user experience.

Describe your approach to effective communication and collaboration in a close-knit environment, emphasizing the importance of sharing ideas and feedback. Share examples of how you have successfully partnered with team members in the past to achieve a common goal, highlighting your interpersonal skills.

Discuss your passion for contributing to open-source projects and your belief in collaborative development. Share your experiences with open-source, whether through contributions, using tools, or understanding community dynamics, reflecting your enthusiasm for the transparency and innovation it fosters.