To thrive as a Software Engineer, Data at Harvey, you should possess at least 3 years of experience in an engineering role, with a strong background in shipping and scaling impactful data-driven products. Experience with data pipelines and backend systems is highly beneficial, and familiarity with search infrastructure or vector databases can give you an edge. Being detail-oriented, having grit, and being flexible to adapt to the early-stage startup culture will also set you up for success!

As a Software Engineer, Data at Harvey, you'll take the reins on various engineering projects aimed at handling extensive datasets. Key responsibilities include developing distributed crawlers, creating data pipelines, and building storage infrastructure while ensuring real-time updates. You will work closely with both technical teams and domain experts to construct complex data systems tailored to legal, finance, and tax sectors, driving the advancement of our innovative products.

The Software Engineer, Data role at Harvey is based in-person in San Francisco, CA. This setup encourages collaboration and innovation within our talented engineering team. We appreciate the synergy that comes from working together physically, and we also offer relocation assistance for successful candidates to make this transition smoother.

Harvey stands out as a unique employer due to its exceptional product market fit and partnerships with industry-leading law firms and professional service providers. We are constantly innovating, working with cutting-edge technology in generative AI. Our investment from renowned firms like Sequoia and Google Ventures further emphasizes our commitment to shaping the future. A world-class team ensures you're surrounded by the best talent in the industry, providing an inspiring environment to learn and grow.

The team culture at Harvey is vibrant and collaborative! As a Software Engineer, Data, you'll join a world-class group of engineers and researchers who are passionate about what they do. We foster an environment that encourages innovation and creativity, with opportunities for continuous learning and professional growth. Grit and resilience are valued qualities here, as we tackle challenges and drive new initiatives in the fast-evolving field of AI.

As a Software Engineer, Data at Harvey, you'll engage in a variety of exciting projects! Some examples include building a comprehensive legal dataset for AI applications, ingesting tax codes from multiple jurisdictions, and scaling data infrastructure to unlock efficient search and retrieval capabilities. You will also explore groundbreaking embedding search technologies to enhance our offerings, making a tangible difference in the way our clients manage their information.

Harvey is committed to attracting and retaining top talent, which is reflected in our compensation package for the Software Engineer, Data position. We offer top-of-market cash and equity compensation, along with additional benefits that prioritize work-life balance and professional development. Our focus on creating an inclusive and rewarding workplace makes Harvey a fantastic place to advance your career.

When discussing your experience with data pipelines, be specific about the projects you've worked on. Highlight how you designed, developed, and optimized data flows, the technologies involved, and how those pipelines improved performance or usability. Explain any challenges you faced and how you overcame them, emphasizing your problem-solving skills.

In an interview for Software Engineer, Data at Harvey, focus on your methodology for evaluating requirements and data characteristics before selecting a storage solution. Elaborate on your experience with different storage options (SQL, NoSQL, cloud-based solutions), the trade-offs between them, and how you ensure data integrity and optimal performance.

Discuss your approach to data security and integrity, which may include implementing encryption, access controls, and regular audits. Share specific examples of how you've implemented these measures in previous roles and the impact they had on the overall security posture of your projects. Your commitment to safeguarding data should be clear.

Discuss any experience you have with legal datasets or similarly complex databases. Talk about the specific tools or frameworks you used, how you structured the data for efficient access, and what challenges arose during those projects. Highlight your familiarity with regulatory compliance in managing sensitive information if applicable.

When answering this question, provide details about the design, technologies used, and the data sources targeted. Discuss the challenges you faced such as handling real-time updates, ensuring performance, and data cleaning processes. Highlight the results of the project and any lessons learned that could be applied to future crawlers.

Emphasize your commitment to professional development, mentioning specific resources like online courses, conferences, or influential publications you follow. Talk about any community involvement, projects, or contributions that demonstrate your proactive approach to staying ahead of industry trends and advancements in data engineering.

Discuss your systematic approach to testing data pipelines, from unit testing individual components to integration testing full workflows. Highlight any testing frameworks or tools you frequently use, and provide examples of how thorough testing has led to successful deployments or uncovered issues before they became critical.

Illustrate your communication skills by sharing experiences where you've collaborated with various stakeholders (e.g., legal experts, clients). Explain how you gathered their requirements and ensured that the data solutions you built met their needs. Emphasize your ability to bridge the gap between technical and non-technical terms effectively.

Share strategies you use to manage your workload effectively in a fast-paced environment. Discuss tools like Agile methodologies or project management software that you find useful. Provide examples of how you’ve successfully managed multiple priorities while maintaining the quality and timely delivery of your projects.

Be genuine in your response, discussing your enthusiasm for the potential of data in driving AI innovations. Share specific aspects, like the opportunity to create impactful user experiences through data-driven insights, and highlight any relevant projects or experiences that shaped your interests in AI and data engineering.