Pearl Talent - Lead Data Engineer - I050

Lead Data Engineer

Pearl works with the top 1% of candidates from around the world and connects them with the best startups in the US and EU. Our clients have raised over $5B in aggregate and are backed by companies like OpenAI, a16z, and Founders Fund. They're looking for the sharpest, hungriest candidates who they can consistently promote and work with over many years. Candidates we've hired have been flown out to the US and EU to work with their clients, and even promoted to roles that match folks onshore in the US.

At Pearl, we're not just another recruiting firm—we connect you with exceptional opportunities to work alongside visionary US and EU founders. Our focus is on placing you in roles where you can grow, make an impact, and build a long-term career. We believe in creating environments where your potential is recognized, your voice matters, and your success is tied to meaningful work—not short-term perks. Joining Pearl means stepping into opportunities that challenge you, support you, and set you up for lasting success.

We are hiring a Lead Data Engineer to design and own the data infrastructure that powers analytics, operations, and AI-driven systems. This role is responsible for consolidating fragmented data sources into a centralized platform while setting the technical direction for data architecture and engineering standards. You will work cross-functionally with engineering, operations, and leadership to ensure data reliability and usability across the organization. The role combines hands-on engineering with technical oversight, particularly across AI-integrated systems. This is ideal for someone who thrives in early-stage environments and can balance pragmatic execution with long-term scalability.

You will play a critical role in transforming fragmented data into a reliable, scalable foundation that powers business decisions and AI workflows. Your work will directly improve data accessibility, system reliability, and operational efficiency across teams. You will enable more accurate candidate matching, better hiring outcomes, and streamlined workflows through robust data pipelines. Additionally, you will elevate engineering quality by setting standards for architecture, testing, and deployment. Your contributions will ensure that AI systems are built on clean, structured, and trustworthy data.

Core Responsibilities

• Design and implement scalable data architecture across ingestion, storage, and serving layers
• Build and maintain ETL/ELT pipelines integrating multiple SaaS platforms and internal systems
• Develop data models supporting analytics, reporting, and AI applications
• Optimize database performance, schema design, and query efficiency
• Establish documentation and data standards for cross-team usability
• Review code and architecture decisions across the engineering team
• Define and enforce best practices for testing, documentation, and deployment
• Identify and prioritize technical debt versus rapid delivery needs
• Troubleshoot and resolve production issues across systems
• Audit LLM-powered pipelines for reliability, cost efficiency, and scalability
• Review prompt structures, structured outputs, and error-handling logic
• Ensure clean and structured data flows into AI systems
• Guide improvements in embeddings, vector storage, and evaluation datasets
• Manage integrations with CRMs, ATS platforms, and other SaaS tools
• Handle API limitations, schema changes, and data inconsistencies
• Ensure high data quality and pipeline reliability
• Support cross-functional teams with data accessibility and insights