Data Engineer

A high-growth Legal-Tech scale-up on a mission to democratize the legal system. Utilizing cutting-edge AI and Natural Language Processing, the company identifies corporate misconduct at scale, providing high-value litigation intelligence to the world’s leading law firms.

Since its founding the company has secured significant backing from premier VCs, reaching Series B while maintaining a rare status as a profitable, mission-driven organization.

Located in a prime Tel Aviv tech hub with immediate train access, the company fosters a collaborative culture with a flexible hybrid work model (2 days WFH).

Responsibilities

• Foundational Architecture: Designing, building, and scaling a Cloud-Native Data Platform tailored for sophisticated AI and LLM workloads.
• Storage & Management: Establishing and maintaining high-performance Data Lakes and Data Warehouses (Snowflake/Databricks) to support massive data ingestion.
• Pipeline Engineering: Developing complex, high-scale Data Pipelines for both Batch and Streaming processes.
• AI/LLM Integration: Operationalizing AI/LLM workloads into production environments, ensuring data availability and integrity for advanced research.
• Database Optimization: Managing and optimizing a diverse database ecosystem, including Postgres, Elasticsearch, Vector DBs, and Graph DBs.

Requirements-

• 7 years of experience as a Data Engineer, with a proven track record of building large-scale data infrastructures – Mandatory.
• Expert-level proficiency in Python for data-intensive applications – Mandatory.
• Cloud Infrastructure: Deep hands-on experience architecting and developing within AWS – Mandatory.
• Extensive experience with Data Lake/Warehouse technologies such as Databricks or Snowflake – Mandatory.
• Ability to design distributed systems from the ground up, considering scalability, performance, and cost-efficiency.
• Proficiency across various SQL and NoSQL paradigms, with specific experience in Vector or Graph databases being a significant advantage.