As a Software Engineer, Data at Harvey, you'll be responsible for developing distributed crawlers, data pipelines, and storage infrastructure to ingest data from various sources. This involves understanding complex datasets and translating them into functional technical systems, contributing to projects that enhance productivity in law, tax, and finance sectors.

To be successful as a Software Engineer, Data at Harvey, candidates should have strong backend engineering skills, experience with data-driven products, and familiarity with search infrastructure. A strong attention to detail and a grit for startup environments are also valued along with at least 3 years of relevant experience.

In this role at Harvey, you'll work on exciting projects such as building structured legal datasets, developing AI applications for law firms, and scaling data infrastructure for efficient document indexing and retrieval. These projects are crucial for redefining how professional services operate.

Harvey not only offers top-of-market cash and equity compensation but also provides relocation assistance for new hires. Our collaborative work culture encourages innovation, and you will work closely with domain experts and other talented engineers, enabling an engaging and supportive environment.

Yes, having experience in early-stage startups can be beneficial for a Software Engineer, Data at Harvey. The role requires flexibility, creativity, and the ability to thrive in a fast-paced environment, and startup experience can enhance these skills.

At Harvey, Software Engineers, Data have numerous opportunities for career growth, including exposure to advanced AI technologies, leadership projects, and collaboration with strategic partners. Our company promotes continuous learning and development, helping you expand your skill set.

Harvey fosters a vibrant work culture that emphasizes collaboration, innovation, and challenge. As a Software Engineer, Data, you'll be part of a diverse team while working on significant projects, making it a rewarding environment for professional and personal growth.

When answering this question, focus on specific projects where you built data pipelines. Describe the technology stack used, challenges faced, and how you ensured robustness and performance in data ingestion.

You might discuss techniques such as data normalization, schema design, and indexing. Share experiences where you translated complex legal or financial data into manageable formats, emphasizing the impact of your decisions.

Use the STAR method (Situation, Task, Action, Result) to share a relevant example, detailing the complexities faced in building data infrastructures and the innovative solutions you applied to resolve them effectively.

Discuss specific methodologies like Agile, Kanban, or personal time management strategies you've used to balance workload, especially in startup settings where projects evolve rapidly.

Highlight any projects where you developed or maintained distributed systems. Discuss technologies used and the importance of performance, fault tolerance, and scalability in your solutions.

Explain your comfort level with APIs, including examples of successful integrations you've handled and how these integrations contributed to data accuracy and usability in your projects.

Mention specific tools and libraries that you've used for data analysis, like SQL, Python, or R, and how you've utilized them for deep insights into complex datasets.

Discuss methods for maintaining data integrity, such as validation rules, testing frameworks, or regular audits, illustrating your commitment to quality in software engineering with real-world examples.

Research Harvey’s products and speak to specific features that excite you, offering insights into how your past experiences and skill set uniquely position you to enhance their product offerings.

Share a thoughtful approach, focusing on user need assessment, system design, and user testing. Emphasize the importance of intuitive interfaces and scalability to handle growing data volumes.